BACKGROUND: The main manifestations of coronavirus disease-2019 (COVID-19) are similar to the man... more BACKGROUND: The main manifestations of coronavirus disease-2019 (COVID-19) are similar to the many other respiratory diseases. In addition, the existence of numerous uncertainties in the prognosis of this condition has multiplied the need to establish a valid and accurate prediction model. This study aimed to develop a diagnostic model based on logistic regression to enhance the diagnostic accuracy of COVID-19. MATERIALS AND METHODS: A standardized diagnostic model was developed on data of 400 patients who were referred to Ayatollah Talleghani Hospital, Abadan, Iran, for the COVID-19 diagnosis. We used the Chi-square correlation coefficient for feature selection, and logistic regression in SPSS V25 software to model the relationship between each of the clinical features. Potentially diagnostic determinants extracted from the patient's history, physical examination, and laboratory and imaging testing were entered in a logistic regression analysis. The discriminative ability of the model was expressed as sensitivity, specificity, accuracy, and area under the curve, respectively. RESULTS: After determining the correlation of each diagnostic regressor with COVID-19 using the Chi-square method, the 15 important regressors were obtained at the level of P < 0.05. The experimental results demonstrated that the binary logistic regression model yielded specificity, sensitivity, and accuracy of 97.3%, 98.8%, and 98.2%, respectively. CONCLUSION: The destructive effects of the COVID-19 outbreak and the shortage of healthcare resources in fighting against this pandemic require increasing attention to using the Clinical Decision Support Systems equipped with supervised learning classification algorithms such as logistic regression.
Background and aim: Due to changes in lifestyle, bariatric surgery is expanding worldwide. Howeve... more Background and aim: Due to changes in lifestyle, bariatric surgery is expanding worldwide. However, this surgery has numerous complications, and early identification of these complications could be essential in assisting patients to have a higher-quality surgery. Machine learning has a significant role in prediction tasks. So far, no systematic review has been carried out on leveraging ML techniques for predicting complications of bariatric surgery. Therefore, this study aims to perform a systematic review for better prediction insight. Materials and methods: This review was conducted in 2023 based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). We searched scientific databases using the inclusion and exclusion criteria to obtain articles. The data extraction form was used to gather data. To analyze the data, we leveraged the narrative synthesis of the quantitative data. Results: Ensemble algorithms outperformed others in large databases, especially at the national registries. Artificial Neural Networks (ANN) performed better than others based on one-single-center database. Also, Deep Belief Networks (DBN) and ANN obtained favorable performance for complications such as diabetes, dyslipidemia, hypertension, thrombosis, leakage, and depression. Conclusion: This review gave us insight into using ensemble and non-ensemble algorithms based on the types of datasets and complications.
Highlights • Machine learning algorithms were leveraged to establish prediction models for the 5-... more Highlights • Machine learning algorithms were leveraged to establish prediction models for the 5-year survival of CRC. • A combination of pathological, laboratory, therapy, socioeconomics, and lifestyle factors was used to predict this topic. • XG-Boost is a satisfactory model for predicting the 5-year survival of CRC. • The pathological and therapy factors are remarkable for prediction on this topic. • This study demonstrated a favourable generalizability of the XG-Boost model in different clinical environments.
Background and aim Pancreatic cancer possesses a high prevalence and mortality rate among other c... more Background and aim Pancreatic cancer possesses a high prevalence and mortality rate among other cancers. Despite the low survival rate of this cancer type, the early prediction of this disease has a crucial role in decreasing the mortality rate and improving the prognosis. So, this study. Materials and methods In this retrospective study, we used 654 alive and dead PC cases to establish the prediction model for PC. The six chosen machine learning algorithms and prognostic factors were utilized to build the prediction models. The importance of the predictive factors was assessed using the relative importance of a high-performing algorithm. Results The XG-Boost with AU-ROC of 0.933 (95% CI= [0.906-0.958]) and AU-ROC of 0.836 (95% CI= [0.789-0.865] in internal and external validation modes were considered as the best-performing model for predicting the mortality risk of PC. The factors, including tumor size, smoking, and chemotherapy, were considered the most influential for prediction. Conclusion The XG-Boost gained more performance efficiency in predicting the mortality risk of PC patients, so this model can promote the clinical solutions that doctors can achieve in healthcare environments to decrease the mortality risk of these patients. Highlights • We developed machine learning models to predict the mortality risk of pancreatic cancer. • XG-Boost demonstrated more competency in predicting mortality risk. • Prognostic factors are essential for predicting the mortality risk of PC. • Based on the external validation results, the clinical applicability of the XG-Boost is almost efficient in other clinical environments. • Some lifestyle factors, such as smoking, have a significant role in predicting the mortality risk on this topic.
Background and aim: Ovarian cancer (OC) is a prevalent and aggressive malignancy that poses a sig... more Background and aim: Ovarian cancer (OC) is a prevalent and aggressive malignancy that poses a significant public health challenge. The lack of preventive strategies for OC increases morbidity, mortality, and other negative consequences. Screening OC through risk prediction could be leveraged as a powerful strategy for preventive purposes that have not received much attention. So, this study aimed to leverage machine learning approaches as predictive assistance solutions to screen high-risk groups of OC and achieve practical preventive purposes. Materials and methods: As this study is data-driven and retrospective in nature, we leveraged 1516 suspicious OC women data from one concentrated database belonging to six clinical settings in Sari City from 2015 to 2019. Six machine learning (ML) algorithms, including XG-Boost, Random Forest (RF), J-48, support vector machine (SVM), K-nearest neighbor (KNN), and artificial neural network (ANN) were leveraged to construct prediction models for OC. To choose the best model for predicting OC, we compared various prediction models built using the area under the receiver characteristic operator curve (AU-ROC). Results: Current experimental results revealed that the XG-Boost with AU-ROC = 0.93 (0.95 CI = [0.91-0.95]) was recognized as the best-performing model for predicting OC. Conclusions: ML approaches possess significant predictive efficiency and interoperability to achieve powerful preventive strategies leveraging OC screening high-risk groups.
Background: Due to the growing number of disabilities in elderly, Attention to this period of lif... more Background: Due to the growing number of disabilities in elderly, Attention to this period of life is essential to be considered. Few studies focused on the physical, mental, disabilities, and disorders affecting the quality of life in elderly people. SA 1 is related to various factors influencing the elderly's life. So, the objective of the current study is to build an intelligent system for SA prediction through ANN 2 algorithms to investigate better all factors affecting the elderly life and promote them. Methods: This study was performed on 1156 SA and non-SA cases. We applied statistical feature reduction method to obtain the best factors predicting the SA. Two models of ANNs with 5, 10, 15, and 20 neurons in hidden layers were used for model construction. Finally, the best ANN configuration was obtained for predicting the SA using sensitivity, specificity, accuracy, and cross-entropy loss function. Results: The study showed that 25 factors correlated with SA at the statistical level of P < 0.05. Assessing all ANN structures resulted in FF-BP 3 algorithm having the configuration of 25-15-1 with accuracy-train of 0.92, accuracy-test of 0.86, and accuracy-validation of 0.87 gaining the best performance over other ANN algorithms. Conclusions: Developing the CDSS for predicting SA has crucial role to effectively inform geriatrics and health care policymakers decision making.
Background and aim: Esophageal cancer (EC) is a highly prevalent and progressive disease. Early p... more Background and aim: Esophageal cancer (EC) is a highly prevalent and progressive disease. Early prediction of EC risk in the population is crucial in preventing this disease and enhancing the overall health of individuals. So far, few studies have been conducted on predicting the EC risk based on the prediction models, and most of them focused on statistical methods. The ML approach obtained efficient predictive insights into the clinical domain. Therefore, this study aims to develop a risk prediction model for EC based on risk factors and by leveraging the ML approach to stratify the high-risk EC people and obtain efficient preventive purposes at the community level. Material and methods: The current retrospective study was performed from 2018 to 2022 in Sari City based on 3256 EC and non-EC cases. The six selected algorithms, including Random Forest (RF), eXtreme Gradient Boosting (XG-Boost), Bagging, K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), and Artificial Neural Networks (ANNs), were used to develop the risk prediction model for EC and achieve the preventive purposes. Results: Comparing the performance efficiency of algorithms revealed that the XG-Boost model gained the best predictability for EC risk with AU-ROC = 0.92 and AU-ROC-test = 0.889 for internal and validation states, respectively. Based on the XG-Boost, the factors, including sex, drinking hot liquids, fruit consumption, achalasia, and vegetable consumption, were considered the five top predictors of EC risk. Conclusion: This study showed that the XG-Boost could provide insight into the early prediction of the EC risk for people and clinical providers to stratify the high-risk group of EC and achieve preventive measures based on modifying the risk factors associated with EC and other clinical solutions.
Introduction: Management and control of
reportable diseases are challenging because
these disease... more Introduction: Management and control of reportable diseases are challenging because these diseases include a large spectrum of infectious conditions that need accurate, precise, and timely reporting. To deal with this problem, an integrated surveillance system using the set of core data architecture principles is crucial to ensure the effective management of data. Therefore, this study aimed to identify the core data architecture requirements for effective management of Coronavirus Disease 2019 (COVID-19), followed by designing a data architecture model. Materials & Methods: This systematic review was conducted in 2020 through searching five databases, including PubMed, Web of Science, Science Direct, and Scopus M, as well as Google scholar search engine to identify metrics for COVID-19 data architecture designing. Moreover, the search formula definition, implication of inclusion and exclusion criteria, search filtering adjustment, and related study identification were performed in this study. Subsequently, the cases identifying the architecture data of the COVID-19 component were systematically extracted and categorized in suitable classes. Finally, the management system of the architecture model of the patient was visualized in this study. Ethics code: IR.ABADANUMS.REC.1399.065 Findings: Out of 398 identified studies, 27 articles met the inclusion criteria. The obtained data were categorized into five classes, including organizations involved in data management (data producer, data users, and decision-makers), data sources, information requirements (11 information classes and 77 data elements), standards (semantic and syntactic), and control quality criteria of the data. Discussions & Conclusions: Implementation of customized data architecture for COVID-19 can increase the potential of the health care systems to prevent the high prevalence of this disease and improve the quality of care through timely and effective health monitoring, accurate epidemiological investigations, clinical decision supports, and health-care policymaking.
Background: Today, the COVID-19 pandemic is ever-increasingly challenging healthcare systems glob... more Background: Today, the COVID-19 pandemic is ever-increasingly challenging healthcare systems globally with many uncertainties and ambiguities regarding disease behavior and outcome prediction. Thus, machine learning (ML) algorithms could be potentially demanding to tackle these challenges. Objectives: The present study aimed to construct and compare two prediction models based on statistical and computational ML algorithms to predict mortality in COVID-19 hospitalized patients and, finally, adopt the best-performing algorithm, accordingly. Methods: Having considered a single-center registry, we scrutinized 482 records of laboratory-confirmed COVID-19 hospitalized patients admitted from February 9, 2020, to December 20, 2020. The most important clinical parameters for COVID-19 mortality prediction were identified using the Phi coefficient technique. In the next step, two statistical and computational ML models, ie, logistic regression (LR) and artificial neural network (ANN), were evaluated through the metrics derived from the confusion matrix. Results: Predictive models were trained using 16 validated features. The results indicated that the best performance pertained to the ANN classifier with a positive predictive value (PPV) of 0.96, a negative predictive value (NPV) of 0.86, the sensitivity of 0.94, specificity of 0.94, and accuracy of 0.93. Conclusions: According to the results, ANN predicted mortality in hospitalized patients with COVID-19 with an acceptable level of accuracy. Therefore, it would be extremely reasonable to develop intelligent decision support systems to early detect high-risk patients, helping clinicians come up with proper interventions.
Background: The worldwide society is currently facing an epidemiological shift due to the signifi... more Background: The worldwide society is currently facing an epidemiological shift due to the significant improvement in life expectancy and increase in the elderly population. This shift requires the public and scientific community to highlight successful aging (SA), as an indicator representing the quality of elderly people's health. SA is a subjective, complex, and multidimensional concept; thus, its meaning or measuring is a difficult task. This study seeks to identify the most affecting factors on SA and fed them as input variables for constructing predictive models using machine learning (ML) algorithms. Methods: Data from 1465 adults aged ≥ 60 years who were referred to health centers in Abadan city (Iran) between 2021 and 2022 were collected by interview. First, binary logistic regression (BLR) was used to identify the main factors influencing SA. Second, eight ML algorithms, including adaptive boosting (AdaBoost), bootstrap aggregating (Bagging), eXtreme Gradient Boosting (XG-Boost), random forest (RF), J-48, multilayered perceptron (MLP), Naïve Bayes (NB), and support vector machine (SVM), were trained to predict SA. Finally, their performance was evaluated using metrics derived from the confusion matrix to determine the best model. Results: The experimental results showed that 44 factors had a meaningful relationship with SA as the output class. In total, the RF algorithm with sensitivity = 0.95 ± 0.01, specificity = 0.94 ± 0.01, accuracy = 0.94 ± 0.005, and F-score = 0.94 ± 0.003 yielded the best performance for predicting SA. Conclusions: Compared to other selected ML methods, the effectiveness of the RF as a bagging algorithm in predicting SA was significantly better. Our developed prediction models can provide, gerontologists, geriatric nursing, healthcare administrators, and policymakers with a reliable and responsive tool to improve elderly outcomes.
Background: Due to advancements in medicine and the elderly population's growth with various disa... more Background: Due to advancements in medicine and the elderly population's growth with various disabilities, attention to QoL among this age group is crucial. Early prediction of the QoL among the elderly by multiple care providers leads to decreased physical and mental disorders and increased social and environmental participation among them by considering all factors affecting it. So far, it is not designed the prediction system for QoL in this regard. Therefore, this study aimed to develop the CDSS based on ANN as an ML technique by considering the physical, psychiatric, and social factors. Methods: In this developmental and applied study, we investigated the 980 cases associated with pleasant and unpleasant elderlies QoL cases. We used the BLR and simple correlation coefficient methods to attain the essential factors affecting the QoL among the elderly. Then three BP configurations, including CF-BP, FF-BP, and E-BP, were compared to get the best model for predicting the QoL. Results: Based on the BLR, the 13 factors were considered the best factors affecting the elderly's QoL at P < 0.05. Comparing all ANN configurations showed that the CF-BP with the 13-16-1 structure with sensitivity = 0.95, specificity = 0.97, accuracy = 0.96, F-Score = 0.96, PPV = 0.95, and NPV = 0.97 gained the best performance for QoL among the elderly. Conclusion: The results of this study showed that the designed CDSS based on the CFBP could be considered an efficient tool for increasing the QoL among the elderly.
Background and Aim: Breast cancer is one of the most common and aggressive malignancies in women.... more Background and Aim: Breast cancer is one of the most common and aggressive malignancies in women. Timely diagnosis of breast cancer plays an important role in preventing the progression of this disease, timely treatment measures, and aftermath reducing the mortality rate of these patients. Machine learning has the potential ability to diagnose diseases quickly and cost-effectively. This study aims to design a CDSS based on the rules extracted from the decision tree algorithm with the best performance to diagnose breast cancer in a timely and effective manner. Materials and Methods: The data of 597 suspected people with breast cancer)255 patients and 342 healthy people(were retrospectively extracted from the electronic database of Ayatollah Taleghani Hospital in Abadan city with 24 characteristics, mainly pertained to lifestyle and medical histories. After selecting the most important variables by using the Chi-square Pearson and one-way analysis of variance)P>0.05(, the performance of selected data mining algorithms including RF, J-48, DS, RT and XG-Boost was evaluated for breast cancer diagnosis in Weka 3.4 software. Finally, the breast cancer diagnostic system was designed based on the best model and through C# programming language and Dot Net Framework V3.5.4. Results: Fourteen variables including personal history of breast cancer, breast sampling, and chest X-ray, high blood pressure, increased LDL blood cholesterol, presence of mass in upper inner quadrant of the breast, hormone therapy with estrogen, hormone therapy with Estrogenprogesterone, family history of breast cancer, age, history of other cancers, waist-to-hip ratio and fruit and vegetable consumption showed a significant relationship with the output class at the P>0.05. Based on the results of the performance evaluation of selected algorithms, the RF model with sensitivity, specificity, accuracy, and F-measure equal to 0.97, 0.99, 0.98, 0.974, respectively, AUC=0.936 had higher performance than other selected algorithms and was suggested as the best model for breast cancer diagnosis. Conclusion: It seems that using modifiable variables such as lifestyle and reproductive-hormonal characteristics as input to the RF algorithm to design the CDSS, can detect breast cancer cases with optimal accuracy. In addition, the proposed system can be effectively adapted in real clinical environments for quick and effective disease diagnosis.
Background and Aim: Colorectal cancer is one of the most common
gastrointestinal cancers among hu... more Background and Aim: Colorectal cancer is one of the most common gastrointestinal cancers among human beings and the most important cause of death in the world. Based on the risk of colorectal cancer for individuals, using an appropriate screening program can help to prevent the disease. Therefore, the purpose of this study was to design a model for screening colorectal cancer based on risk factors to increase the survival rate of the disease on the one hand and to reduce the mortality rate on the other. Materials and Methods: By reviewing articles and patients' records, 38 risk factors were detected. To determine the most important risk factors clinically, CVR(content validity ratio) was used; and considering the collected data, Spearman correlation coefficient and logistic regression analysis were applied for statistical analyses. Then, four algorithms -- J-48, J-RIP, PART and REP-Tree -- were used for data mining and rule generation. Finally, the most common model was obtained based on comparing the performance of the algorithms. Results: After comparing the performance of algorithms, the J-48 algorithm with an F-Measure of 0.889 was found to be better than the others. Conclusion: The results of evaluating J-48 data mining algorithm performance showed that this algorithm could be considered as the most appropriate model for colorectal cancer risk prediction.
Background coronavirus dis a predictive mo who would nee Methods: In 2020, were ana and Naïve Bay... more Background coronavirus dis a predictive mo who would nee Methods: In 2020, were ana and Naïve Bay comparing the MCC, and Kap Results: Pred taste, rhinorrhe cell count, card and AUC = 0.8 Conclusion: 19 in-hospital p of MV resource
Background: Colorectal Cancer (CRC) is the most prevalent digestive system-related cancer and has... more Background: Colorectal Cancer (CRC) is the most prevalent digestive system-related cancer and has become one of the deadliest diseases worldwide. Given the poor prognosis of CRC, it is of great importance to make a more accurate prediction of this disease. Early CRC detection using computational technologies can significantly improve the overall survival possibility of patients. Hence this study was aimed to develop a fuzzy logic-based clinical decision support system (FL-based CDSS) for the detection of CRC patients. Methods: This study was conducted in 2020 using the data related to CRC and non-CRC patients, which included the 1162 cases in the Masoud internal clinic, Tehran, Iran. The chi-square method was used to determine the most important risk factors in predicting CRC. Furthermore, the C4.5 decision tree was used to extract the rules. Finally, the FL-based CDSS was designed in a MATLAB environment and its performance was evaluated by a confusion matrix. Results: Eleven features were selected as the most important factors. After fuzzification of the qualitative variables and evaluation of the decision support system (DSS) using the confusion matrix, the accuracy, specificity, and sensitivity of the system was yielded 0.96, 0.97, and 0.96, respectively. Conclusion: We concluded that developing the CDSS in this field can provide an earlier diagnosis of CRC, leading to a timely treatment, which could decrease the CRC mortality rate in the community.
COVID-19 is the most dangerous and highly contagious disease with unknown clinical aspects. Curre... more COVID-19 is the most dangerous and highly contagious disease with unknown clinical aspects. Currently, in the lack of effective treatment or vaccine, the early diagnosis of COVID-19 is the key to its treatment, implement early isolation and quarantine strategies. →What this article adds: Intelligent and machine learning techniques can be used as an alternative solution in the battle against the COVID-19 pandemic. In this study, we utilized the different decision tree algorithms and compared their performance in diagnosing COVID-19. Based on the results, it can be concluded that the decision tree algorithms can be used as a potential diagnostic model for earlier detection of COVID-19.
Automated breast cancer diagnosis based on ... [2] Comparison of the performance of machine learn... more Automated breast cancer diagnosis based on ... [2] Comparison of the performance of machine learning algorithms ... [3] Breast cancer population screening program results in ... [4] Aggressive behavior of Her-2 positive colloid ... [5] A paradigm shift toward a more aggressive ... [6] Metastatic ovarian cancer spreading into mammary ducts ... [7] Advanced stage at diagnosis and worse clinicopathologic ... [8] Late-stage diagnosis and associated factors among ... [9] Perspectives of patients, family members, and health ... [10] New insights into the screening, prompt ... [11] A comparative study of mammography, sonography ... [12] Validity and reliability of health belief model ... [13] Clinical breast examination and breast ... [14] Integration of clinical variables for the prediction of ... [15] Predicting breast cancer metastasis by ... [16] Computational radiology in breast cancer screening ... [17] Drug and hormone resistance in ... [18] Handbook of research on applications ... [19] Personalized pancreatic cancer management ... [20] Application of machine learning techniques ... [21] Machine learning with applications in breast ... [22] A machine learning approach to uncovering ... [23] Prediction of breast cancer using rule ... [24] Breast cancer diagnosis using feature ... [25] Breast cancer disease classification ... [26] Survival prediction of patients with breast ... [27] Integration of data mining classification ... [28] Imbalanced machine learning based techniques ... [29] A hybrid supervised machine learning ... [30] Applications of machine learning techniques ... [31] Analysis of breast cancer detection using ... [32] Comparison of decision tree methods for breast ... [33] Prediction of breast cancer recurrence ... [34] Comparative study on different classification ... [35] Classifying breast cancer by using ... [36] An analysis of classification of breast ... Aims Breast cancer represents one of the most prevalent cancers and is also the main cause of cancer-related deaths in women globally. Thus, this study was aimed to construct and compare the performance of several rule-based machine learning algorithms in predicting breast cancer. Instrument & Methods The data were collected from the Breast Cancer Registry database in the Ayatollah Taleghani Hospital, Abadan, Iran, from December 2017 to January 2021 and had information from 949 non-breast cancer and 554 breast cancer cases. Then the mean values and K-nearest neighborhood algorithm were used for replacing the lost quantitative and qualitative data fields, respectively. In the next step, the Chi-square test and binary logistic regression were used for feature selection. Finally, the best rule-based machine learning algorithm was obtained based on comparing different evaluation criteria. The Rapid Miner Studio 7.1.1 and Weka 3.9 software were utilized. Findings As a result of feature selection the nine variables were considered as the most important variables for data mining. Generally, the results of comparing rule-based machine learning demonstrated that the J-48 algorithm with an accuracy of 0.991, F-measure of 0.987, and also AUC of 0.9997 had a better performance than others. Conclusion It's found that J-48 facilitates a reasonable level of accuracy for correct BC risk prediction. We believe it would be beneficial for designing intelligent decision support systems for the early detection of high-risk patients that will be used to inform proper interventions by the clinicians.
The rapid worldwide outbreak of COVID-19 has posed serious and unprecedented challenges to health... more The rapid worldwide outbreak of COVID-19 has posed serious and unprecedented challenges to healthcare systems in predicting disease behavior, consequences and resource utilization. Therefore, predicting the Length of Stay (LOS) is necessary to ensure optimal allocate of scarce hospital resources. The purpose of this research was to construct a model for predicting COVID-19 patients' hospital LOS by multiple Machine Learning (ML) algorithms. Using a single-center registry, we studied the records of 1225 laboratory-confirmed COVID-19 hospitalized patients from February 9, 2020, to December 20, 2020. The most important clinical parameters in the COVID-19 LOS prediction were identified with a correlation coefficient at the P-value< 0.2. Then, the prediction models were developed based on seven ML techniques according to selected variables. Finally, to evaluate the performances of those models several standard quantitative measures includes accuracy, sensitivity, specificity and ROC curve were used to evaluate the proposed predictive models. After implementing feature selection, a total of 20 variables was identified as the most relevant predictors to build the prediction models. The results indicated that the best performance belonged to the Support Vector Machine (SVM) algorithm with the mean accuracy of 99.5%, mean specificity of 99.7%, mean sensitivity of 99.4%, and the standard deviation of 1.2. The SVM provided a reasonable level of accuracy and certainty in predicting the LOS in COVID-19 patients and potentially facilitates hospital bed management, turnover and optimized resource allocation.
BACKGROUND: Improving the physical, psychological, and social factors in the elderly significantl... more BACKGROUND: Improving the physical, psychological, and social factors in the elderly significantly increases the QoL 1 among them. This study aims to identify the crucial factors for predicting QoL among the elderly using statistical methods. MATERIALS AND METHODS: In this study, 980 samples related to the elderly with favorable and unfavorable QoL were investigated. The elderly's QoL was investigated using a qualitative and self-assessment questionnaire that measured the QoL among them by five Likert spectrum and independent factors. The Chi-square test and eta coefficient were used to determine the relationship between each predicting factor of the elderly's QoL in SPSS V 25 software. Finally, we used the Enter and Forward LR methods to determine the correlation of influential factors in the presence of other variables. RESULTS: The study showed that 20 variables gained a significant relationship with the quality of life of the elderly at P < 0.05. The study results showed that the degree of dependence (P = 0.03), diabetes mellitus (P = 0.03), formal and informal social relationships (P = 0.01 and P = 0.02), ability to play an emotional role (P = 0.03), physical performance (P = 0.01), heart diseases and arterial blood pressure (P = 0.02), and cancer (P = 0.01) have favorable predictive power in predicting the QoL among the elderly. CONCLUSION: Attempts to identify and modify the important factors affecting the elderly's QoL have a significant role in improving the QoL and life satisfaction in this age group people. This study showed that the statistical methods have a pleasant capability to discover the factors associated with the elderly's QoL with high performance in this regard.
BACKGROUND: The main manifestations of coronavirus disease-2019 (COVID-19) are similar to the man... more BACKGROUND: The main manifestations of coronavirus disease-2019 (COVID-19) are similar to the many other respiratory diseases. In addition, the existence of numerous uncertainties in the prognosis of this condition has multiplied the need to establish a valid and accurate prediction model. This study aimed to develop a diagnostic model based on logistic regression to enhance the diagnostic accuracy of COVID-19. MATERIALS AND METHODS: A standardized diagnostic model was developed on data of 400 patients who were referred to Ayatollah Talleghani Hospital, Abadan, Iran, for the COVID-19 diagnosis. We used the Chi-square correlation coefficient for feature selection, and logistic regression in SPSS V25 software to model the relationship between each of the clinical features. Potentially diagnostic determinants extracted from the patient's history, physical examination, and laboratory and imaging testing were entered in a logistic regression analysis. The discriminative ability of the model was expressed as sensitivity, specificity, accuracy, and area under the curve, respectively. RESULTS: After determining the correlation of each diagnostic regressor with COVID-19 using the Chi-square method, the 15 important regressors were obtained at the level of P < 0.05. The experimental results demonstrated that the binary logistic regression model yielded specificity, sensitivity, and accuracy of 97.3%, 98.8%, and 98.2%, respectively. CONCLUSION: The destructive effects of the COVID-19 outbreak and the shortage of healthcare resources in fighting against this pandemic require increasing attention to using the Clinical Decision Support Systems equipped with supervised learning classification algorithms such as logistic regression.
Background and aim: Due to changes in lifestyle, bariatric surgery is expanding worldwide. Howeve... more Background and aim: Due to changes in lifestyle, bariatric surgery is expanding worldwide. However, this surgery has numerous complications, and early identification of these complications could be essential in assisting patients to have a higher-quality surgery. Machine learning has a significant role in prediction tasks. So far, no systematic review has been carried out on leveraging ML techniques for predicting complications of bariatric surgery. Therefore, this study aims to perform a systematic review for better prediction insight. Materials and methods: This review was conducted in 2023 based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). We searched scientific databases using the inclusion and exclusion criteria to obtain articles. The data extraction form was used to gather data. To analyze the data, we leveraged the narrative synthesis of the quantitative data. Results: Ensemble algorithms outperformed others in large databases, especially at the national registries. Artificial Neural Networks (ANN) performed better than others based on one-single-center database. Also, Deep Belief Networks (DBN) and ANN obtained favorable performance for complications such as diabetes, dyslipidemia, hypertension, thrombosis, leakage, and depression. Conclusion: This review gave us insight into using ensemble and non-ensemble algorithms based on the types of datasets and complications.
Highlights • Machine learning algorithms were leveraged to establish prediction models for the 5-... more Highlights • Machine learning algorithms were leveraged to establish prediction models for the 5-year survival of CRC. • A combination of pathological, laboratory, therapy, socioeconomics, and lifestyle factors was used to predict this topic. • XG-Boost is a satisfactory model for predicting the 5-year survival of CRC. • The pathological and therapy factors are remarkable for prediction on this topic. • This study demonstrated a favourable generalizability of the XG-Boost model in different clinical environments.
Background and aim Pancreatic cancer possesses a high prevalence and mortality rate among other c... more Background and aim Pancreatic cancer possesses a high prevalence and mortality rate among other cancers. Despite the low survival rate of this cancer type, the early prediction of this disease has a crucial role in decreasing the mortality rate and improving the prognosis. So, this study. Materials and methods In this retrospective study, we used 654 alive and dead PC cases to establish the prediction model for PC. The six chosen machine learning algorithms and prognostic factors were utilized to build the prediction models. The importance of the predictive factors was assessed using the relative importance of a high-performing algorithm. Results The XG-Boost with AU-ROC of 0.933 (95% CI= [0.906-0.958]) and AU-ROC of 0.836 (95% CI= [0.789-0.865] in internal and external validation modes were considered as the best-performing model for predicting the mortality risk of PC. The factors, including tumor size, smoking, and chemotherapy, were considered the most influential for prediction. Conclusion The XG-Boost gained more performance efficiency in predicting the mortality risk of PC patients, so this model can promote the clinical solutions that doctors can achieve in healthcare environments to decrease the mortality risk of these patients. Highlights • We developed machine learning models to predict the mortality risk of pancreatic cancer. • XG-Boost demonstrated more competency in predicting mortality risk. • Prognostic factors are essential for predicting the mortality risk of PC. • Based on the external validation results, the clinical applicability of the XG-Boost is almost efficient in other clinical environments. • Some lifestyle factors, such as smoking, have a significant role in predicting the mortality risk on this topic.
Background and aim: Ovarian cancer (OC) is a prevalent and aggressive malignancy that poses a sig... more Background and aim: Ovarian cancer (OC) is a prevalent and aggressive malignancy that poses a significant public health challenge. The lack of preventive strategies for OC increases morbidity, mortality, and other negative consequences. Screening OC through risk prediction could be leveraged as a powerful strategy for preventive purposes that have not received much attention. So, this study aimed to leverage machine learning approaches as predictive assistance solutions to screen high-risk groups of OC and achieve practical preventive purposes. Materials and methods: As this study is data-driven and retrospective in nature, we leveraged 1516 suspicious OC women data from one concentrated database belonging to six clinical settings in Sari City from 2015 to 2019. Six machine learning (ML) algorithms, including XG-Boost, Random Forest (RF), J-48, support vector machine (SVM), K-nearest neighbor (KNN), and artificial neural network (ANN) were leveraged to construct prediction models for OC. To choose the best model for predicting OC, we compared various prediction models built using the area under the receiver characteristic operator curve (AU-ROC). Results: Current experimental results revealed that the XG-Boost with AU-ROC = 0.93 (0.95 CI = [0.91-0.95]) was recognized as the best-performing model for predicting OC. Conclusions: ML approaches possess significant predictive efficiency and interoperability to achieve powerful preventive strategies leveraging OC screening high-risk groups.
Background: Due to the growing number of disabilities in elderly, Attention to this period of lif... more Background: Due to the growing number of disabilities in elderly, Attention to this period of life is essential to be considered. Few studies focused on the physical, mental, disabilities, and disorders affecting the quality of life in elderly people. SA 1 is related to various factors influencing the elderly's life. So, the objective of the current study is to build an intelligent system for SA prediction through ANN 2 algorithms to investigate better all factors affecting the elderly life and promote them. Methods: This study was performed on 1156 SA and non-SA cases. We applied statistical feature reduction method to obtain the best factors predicting the SA. Two models of ANNs with 5, 10, 15, and 20 neurons in hidden layers were used for model construction. Finally, the best ANN configuration was obtained for predicting the SA using sensitivity, specificity, accuracy, and cross-entropy loss function. Results: The study showed that 25 factors correlated with SA at the statistical level of P < 0.05. Assessing all ANN structures resulted in FF-BP 3 algorithm having the configuration of 25-15-1 with accuracy-train of 0.92, accuracy-test of 0.86, and accuracy-validation of 0.87 gaining the best performance over other ANN algorithms. Conclusions: Developing the CDSS for predicting SA has crucial role to effectively inform geriatrics and health care policymakers decision making.
Background and aim: Esophageal cancer (EC) is a highly prevalent and progressive disease. Early p... more Background and aim: Esophageal cancer (EC) is a highly prevalent and progressive disease. Early prediction of EC risk in the population is crucial in preventing this disease and enhancing the overall health of individuals. So far, few studies have been conducted on predicting the EC risk based on the prediction models, and most of them focused on statistical methods. The ML approach obtained efficient predictive insights into the clinical domain. Therefore, this study aims to develop a risk prediction model for EC based on risk factors and by leveraging the ML approach to stratify the high-risk EC people and obtain efficient preventive purposes at the community level. Material and methods: The current retrospective study was performed from 2018 to 2022 in Sari City based on 3256 EC and non-EC cases. The six selected algorithms, including Random Forest (RF), eXtreme Gradient Boosting (XG-Boost), Bagging, K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), and Artificial Neural Networks (ANNs), were used to develop the risk prediction model for EC and achieve the preventive purposes. Results: Comparing the performance efficiency of algorithms revealed that the XG-Boost model gained the best predictability for EC risk with AU-ROC = 0.92 and AU-ROC-test = 0.889 for internal and validation states, respectively. Based on the XG-Boost, the factors, including sex, drinking hot liquids, fruit consumption, achalasia, and vegetable consumption, were considered the five top predictors of EC risk. Conclusion: This study showed that the XG-Boost could provide insight into the early prediction of the EC risk for people and clinical providers to stratify the high-risk group of EC and achieve preventive measures based on modifying the risk factors associated with EC and other clinical solutions.
Introduction: Management and control of
reportable diseases are challenging because
these disease... more Introduction: Management and control of reportable diseases are challenging because these diseases include a large spectrum of infectious conditions that need accurate, precise, and timely reporting. To deal with this problem, an integrated surveillance system using the set of core data architecture principles is crucial to ensure the effective management of data. Therefore, this study aimed to identify the core data architecture requirements for effective management of Coronavirus Disease 2019 (COVID-19), followed by designing a data architecture model. Materials & Methods: This systematic review was conducted in 2020 through searching five databases, including PubMed, Web of Science, Science Direct, and Scopus M, as well as Google scholar search engine to identify metrics for COVID-19 data architecture designing. Moreover, the search formula definition, implication of inclusion and exclusion criteria, search filtering adjustment, and related study identification were performed in this study. Subsequently, the cases identifying the architecture data of the COVID-19 component were systematically extracted and categorized in suitable classes. Finally, the management system of the architecture model of the patient was visualized in this study. Ethics code: IR.ABADANUMS.REC.1399.065 Findings: Out of 398 identified studies, 27 articles met the inclusion criteria. The obtained data were categorized into five classes, including organizations involved in data management (data producer, data users, and decision-makers), data sources, information requirements (11 information classes and 77 data elements), standards (semantic and syntactic), and control quality criteria of the data. Discussions & Conclusions: Implementation of customized data architecture for COVID-19 can increase the potential of the health care systems to prevent the high prevalence of this disease and improve the quality of care through timely and effective health monitoring, accurate epidemiological investigations, clinical decision supports, and health-care policymaking.
Background: Today, the COVID-19 pandemic is ever-increasingly challenging healthcare systems glob... more Background: Today, the COVID-19 pandemic is ever-increasingly challenging healthcare systems globally with many uncertainties and ambiguities regarding disease behavior and outcome prediction. Thus, machine learning (ML) algorithms could be potentially demanding to tackle these challenges. Objectives: The present study aimed to construct and compare two prediction models based on statistical and computational ML algorithms to predict mortality in COVID-19 hospitalized patients and, finally, adopt the best-performing algorithm, accordingly. Methods: Having considered a single-center registry, we scrutinized 482 records of laboratory-confirmed COVID-19 hospitalized patients admitted from February 9, 2020, to December 20, 2020. The most important clinical parameters for COVID-19 mortality prediction were identified using the Phi coefficient technique. In the next step, two statistical and computational ML models, ie, logistic regression (LR) and artificial neural network (ANN), were evaluated through the metrics derived from the confusion matrix. Results: Predictive models were trained using 16 validated features. The results indicated that the best performance pertained to the ANN classifier with a positive predictive value (PPV) of 0.96, a negative predictive value (NPV) of 0.86, the sensitivity of 0.94, specificity of 0.94, and accuracy of 0.93. Conclusions: According to the results, ANN predicted mortality in hospitalized patients with COVID-19 with an acceptable level of accuracy. Therefore, it would be extremely reasonable to develop intelligent decision support systems to early detect high-risk patients, helping clinicians come up with proper interventions.
Background: The worldwide society is currently facing an epidemiological shift due to the signifi... more Background: The worldwide society is currently facing an epidemiological shift due to the significant improvement in life expectancy and increase in the elderly population. This shift requires the public and scientific community to highlight successful aging (SA), as an indicator representing the quality of elderly people's health. SA is a subjective, complex, and multidimensional concept; thus, its meaning or measuring is a difficult task. This study seeks to identify the most affecting factors on SA and fed them as input variables for constructing predictive models using machine learning (ML) algorithms. Methods: Data from 1465 adults aged ≥ 60 years who were referred to health centers in Abadan city (Iran) between 2021 and 2022 were collected by interview. First, binary logistic regression (BLR) was used to identify the main factors influencing SA. Second, eight ML algorithms, including adaptive boosting (AdaBoost), bootstrap aggregating (Bagging), eXtreme Gradient Boosting (XG-Boost), random forest (RF), J-48, multilayered perceptron (MLP), Naïve Bayes (NB), and support vector machine (SVM), were trained to predict SA. Finally, their performance was evaluated using metrics derived from the confusion matrix to determine the best model. Results: The experimental results showed that 44 factors had a meaningful relationship with SA as the output class. In total, the RF algorithm with sensitivity = 0.95 ± 0.01, specificity = 0.94 ± 0.01, accuracy = 0.94 ± 0.005, and F-score = 0.94 ± 0.003 yielded the best performance for predicting SA. Conclusions: Compared to other selected ML methods, the effectiveness of the RF as a bagging algorithm in predicting SA was significantly better. Our developed prediction models can provide, gerontologists, geriatric nursing, healthcare administrators, and policymakers with a reliable and responsive tool to improve elderly outcomes.
Background: Due to advancements in medicine and the elderly population's growth with various disa... more Background: Due to advancements in medicine and the elderly population's growth with various disabilities, attention to QoL among this age group is crucial. Early prediction of the QoL among the elderly by multiple care providers leads to decreased physical and mental disorders and increased social and environmental participation among them by considering all factors affecting it. So far, it is not designed the prediction system for QoL in this regard. Therefore, this study aimed to develop the CDSS based on ANN as an ML technique by considering the physical, psychiatric, and social factors. Methods: In this developmental and applied study, we investigated the 980 cases associated with pleasant and unpleasant elderlies QoL cases. We used the BLR and simple correlation coefficient methods to attain the essential factors affecting the QoL among the elderly. Then three BP configurations, including CF-BP, FF-BP, and E-BP, were compared to get the best model for predicting the QoL. Results: Based on the BLR, the 13 factors were considered the best factors affecting the elderly's QoL at P < 0.05. Comparing all ANN configurations showed that the CF-BP with the 13-16-1 structure with sensitivity = 0.95, specificity = 0.97, accuracy = 0.96, F-Score = 0.96, PPV = 0.95, and NPV = 0.97 gained the best performance for QoL among the elderly. Conclusion: The results of this study showed that the designed CDSS based on the CFBP could be considered an efficient tool for increasing the QoL among the elderly.
Background and Aim: Breast cancer is one of the most common and aggressive malignancies in women.... more Background and Aim: Breast cancer is one of the most common and aggressive malignancies in women. Timely diagnosis of breast cancer plays an important role in preventing the progression of this disease, timely treatment measures, and aftermath reducing the mortality rate of these patients. Machine learning has the potential ability to diagnose diseases quickly and cost-effectively. This study aims to design a CDSS based on the rules extracted from the decision tree algorithm with the best performance to diagnose breast cancer in a timely and effective manner. Materials and Methods: The data of 597 suspected people with breast cancer)255 patients and 342 healthy people(were retrospectively extracted from the electronic database of Ayatollah Taleghani Hospital in Abadan city with 24 characteristics, mainly pertained to lifestyle and medical histories. After selecting the most important variables by using the Chi-square Pearson and one-way analysis of variance)P>0.05(, the performance of selected data mining algorithms including RF, J-48, DS, RT and XG-Boost was evaluated for breast cancer diagnosis in Weka 3.4 software. Finally, the breast cancer diagnostic system was designed based on the best model and through C# programming language and Dot Net Framework V3.5.4. Results: Fourteen variables including personal history of breast cancer, breast sampling, and chest X-ray, high blood pressure, increased LDL blood cholesterol, presence of mass in upper inner quadrant of the breast, hormone therapy with estrogen, hormone therapy with Estrogenprogesterone, family history of breast cancer, age, history of other cancers, waist-to-hip ratio and fruit and vegetable consumption showed a significant relationship with the output class at the P>0.05. Based on the results of the performance evaluation of selected algorithms, the RF model with sensitivity, specificity, accuracy, and F-measure equal to 0.97, 0.99, 0.98, 0.974, respectively, AUC=0.936 had higher performance than other selected algorithms and was suggested as the best model for breast cancer diagnosis. Conclusion: It seems that using modifiable variables such as lifestyle and reproductive-hormonal characteristics as input to the RF algorithm to design the CDSS, can detect breast cancer cases with optimal accuracy. In addition, the proposed system can be effectively adapted in real clinical environments for quick and effective disease diagnosis.
Background and Aim: Colorectal cancer is one of the most common
gastrointestinal cancers among hu... more Background and Aim: Colorectal cancer is one of the most common gastrointestinal cancers among human beings and the most important cause of death in the world. Based on the risk of colorectal cancer for individuals, using an appropriate screening program can help to prevent the disease. Therefore, the purpose of this study was to design a model for screening colorectal cancer based on risk factors to increase the survival rate of the disease on the one hand and to reduce the mortality rate on the other. Materials and Methods: By reviewing articles and patients' records, 38 risk factors were detected. To determine the most important risk factors clinically, CVR(content validity ratio) was used; and considering the collected data, Spearman correlation coefficient and logistic regression analysis were applied for statistical analyses. Then, four algorithms -- J-48, J-RIP, PART and REP-Tree -- were used for data mining and rule generation. Finally, the most common model was obtained based on comparing the performance of the algorithms. Results: After comparing the performance of algorithms, the J-48 algorithm with an F-Measure of 0.889 was found to be better than the others. Conclusion: The results of evaluating J-48 data mining algorithm performance showed that this algorithm could be considered as the most appropriate model for colorectal cancer risk prediction.
Background coronavirus dis a predictive mo who would nee Methods: In 2020, were ana and Naïve Bay... more Background coronavirus dis a predictive mo who would nee Methods: In 2020, were ana and Naïve Bay comparing the MCC, and Kap Results: Pred taste, rhinorrhe cell count, card and AUC = 0.8 Conclusion: 19 in-hospital p of MV resource
Background: Colorectal Cancer (CRC) is the most prevalent digestive system-related cancer and has... more Background: Colorectal Cancer (CRC) is the most prevalent digestive system-related cancer and has become one of the deadliest diseases worldwide. Given the poor prognosis of CRC, it is of great importance to make a more accurate prediction of this disease. Early CRC detection using computational technologies can significantly improve the overall survival possibility of patients. Hence this study was aimed to develop a fuzzy logic-based clinical decision support system (FL-based CDSS) for the detection of CRC patients. Methods: This study was conducted in 2020 using the data related to CRC and non-CRC patients, which included the 1162 cases in the Masoud internal clinic, Tehran, Iran. The chi-square method was used to determine the most important risk factors in predicting CRC. Furthermore, the C4.5 decision tree was used to extract the rules. Finally, the FL-based CDSS was designed in a MATLAB environment and its performance was evaluated by a confusion matrix. Results: Eleven features were selected as the most important factors. After fuzzification of the qualitative variables and evaluation of the decision support system (DSS) using the confusion matrix, the accuracy, specificity, and sensitivity of the system was yielded 0.96, 0.97, and 0.96, respectively. Conclusion: We concluded that developing the CDSS in this field can provide an earlier diagnosis of CRC, leading to a timely treatment, which could decrease the CRC mortality rate in the community.
COVID-19 is the most dangerous and highly contagious disease with unknown clinical aspects. Curre... more COVID-19 is the most dangerous and highly contagious disease with unknown clinical aspects. Currently, in the lack of effective treatment or vaccine, the early diagnosis of COVID-19 is the key to its treatment, implement early isolation and quarantine strategies. →What this article adds: Intelligent and machine learning techniques can be used as an alternative solution in the battle against the COVID-19 pandemic. In this study, we utilized the different decision tree algorithms and compared their performance in diagnosing COVID-19. Based on the results, it can be concluded that the decision tree algorithms can be used as a potential diagnostic model for earlier detection of COVID-19.
Automated breast cancer diagnosis based on ... [2] Comparison of the performance of machine learn... more Automated breast cancer diagnosis based on ... [2] Comparison of the performance of machine learning algorithms ... [3] Breast cancer population screening program results in ... [4] Aggressive behavior of Her-2 positive colloid ... [5] A paradigm shift toward a more aggressive ... [6] Metastatic ovarian cancer spreading into mammary ducts ... [7] Advanced stage at diagnosis and worse clinicopathologic ... [8] Late-stage diagnosis and associated factors among ... [9] Perspectives of patients, family members, and health ... [10] New insights into the screening, prompt ... [11] A comparative study of mammography, sonography ... [12] Validity and reliability of health belief model ... [13] Clinical breast examination and breast ... [14] Integration of clinical variables for the prediction of ... [15] Predicting breast cancer metastasis by ... [16] Computational radiology in breast cancer screening ... [17] Drug and hormone resistance in ... [18] Handbook of research on applications ... [19] Personalized pancreatic cancer management ... [20] Application of machine learning techniques ... [21] Machine learning with applications in breast ... [22] A machine learning approach to uncovering ... [23] Prediction of breast cancer using rule ... [24] Breast cancer diagnosis using feature ... [25] Breast cancer disease classification ... [26] Survival prediction of patients with breast ... [27] Integration of data mining classification ... [28] Imbalanced machine learning based techniques ... [29] A hybrid supervised machine learning ... [30] Applications of machine learning techniques ... [31] Analysis of breast cancer detection using ... [32] Comparison of decision tree methods for breast ... [33] Prediction of breast cancer recurrence ... [34] Comparative study on different classification ... [35] Classifying breast cancer by using ... [36] An analysis of classification of breast ... Aims Breast cancer represents one of the most prevalent cancers and is also the main cause of cancer-related deaths in women globally. Thus, this study was aimed to construct and compare the performance of several rule-based machine learning algorithms in predicting breast cancer. Instrument & Methods The data were collected from the Breast Cancer Registry database in the Ayatollah Taleghani Hospital, Abadan, Iran, from December 2017 to January 2021 and had information from 949 non-breast cancer and 554 breast cancer cases. Then the mean values and K-nearest neighborhood algorithm were used for replacing the lost quantitative and qualitative data fields, respectively. In the next step, the Chi-square test and binary logistic regression were used for feature selection. Finally, the best rule-based machine learning algorithm was obtained based on comparing different evaluation criteria. The Rapid Miner Studio 7.1.1 and Weka 3.9 software were utilized. Findings As a result of feature selection the nine variables were considered as the most important variables for data mining. Generally, the results of comparing rule-based machine learning demonstrated that the J-48 algorithm with an accuracy of 0.991, F-measure of 0.987, and also AUC of 0.9997 had a better performance than others. Conclusion It's found that J-48 facilitates a reasonable level of accuracy for correct BC risk prediction. We believe it would be beneficial for designing intelligent decision support systems for the early detection of high-risk patients that will be used to inform proper interventions by the clinicians.
The rapid worldwide outbreak of COVID-19 has posed serious and unprecedented challenges to health... more The rapid worldwide outbreak of COVID-19 has posed serious and unprecedented challenges to healthcare systems in predicting disease behavior, consequences and resource utilization. Therefore, predicting the Length of Stay (LOS) is necessary to ensure optimal allocate of scarce hospital resources. The purpose of this research was to construct a model for predicting COVID-19 patients' hospital LOS by multiple Machine Learning (ML) algorithms. Using a single-center registry, we studied the records of 1225 laboratory-confirmed COVID-19 hospitalized patients from February 9, 2020, to December 20, 2020. The most important clinical parameters in the COVID-19 LOS prediction were identified with a correlation coefficient at the P-value< 0.2. Then, the prediction models were developed based on seven ML techniques according to selected variables. Finally, to evaluate the performances of those models several standard quantitative measures includes accuracy, sensitivity, specificity and ROC curve were used to evaluate the proposed predictive models. After implementing feature selection, a total of 20 variables was identified as the most relevant predictors to build the prediction models. The results indicated that the best performance belonged to the Support Vector Machine (SVM) algorithm with the mean accuracy of 99.5%, mean specificity of 99.7%, mean sensitivity of 99.4%, and the standard deviation of 1.2. The SVM provided a reasonable level of accuracy and certainty in predicting the LOS in COVID-19 patients and potentially facilitates hospital bed management, turnover and optimized resource allocation.
BACKGROUND: Improving the physical, psychological, and social factors in the elderly significantl... more BACKGROUND: Improving the physical, psychological, and social factors in the elderly significantly increases the QoL 1 among them. This study aims to identify the crucial factors for predicting QoL among the elderly using statistical methods. MATERIALS AND METHODS: In this study, 980 samples related to the elderly with favorable and unfavorable QoL were investigated. The elderly's QoL was investigated using a qualitative and self-assessment questionnaire that measured the QoL among them by five Likert spectrum and independent factors. The Chi-square test and eta coefficient were used to determine the relationship between each predicting factor of the elderly's QoL in SPSS V 25 software. Finally, we used the Enter and Forward LR methods to determine the correlation of influential factors in the presence of other variables. RESULTS: The study showed that 20 variables gained a significant relationship with the quality of life of the elderly at P < 0.05. The study results showed that the degree of dependence (P = 0.03), diabetes mellitus (P = 0.03), formal and informal social relationships (P = 0.01 and P = 0.02), ability to play an emotional role (P = 0.03), physical performance (P = 0.01), heart diseases and arterial blood pressure (P = 0.02), and cancer (P = 0.01) have favorable predictive power in predicting the QoL among the elderly. CONCLUSION: Attempts to identify and modify the important factors affecting the elderly's QoL have a significant role in improving the QoL and life satisfaction in this age group people. This study showed that the statistical methods have a pleasant capability to discover the factors associated with the elderly's QoL with high performance in this regard.
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Papers by Raoof Nopour
reportable diseases are challenging because
these diseases include a large spectrum of
infectious conditions that need accurate,
precise, and timely reporting. To deal with
this problem, an integrated surveillance
system using the set of core data architecture
principles is crucial to ensure the effective
management of data. Therefore, this study
aimed to identify the core data architecture
requirements for effective management of
Coronavirus Disease 2019 (COVID-19),
followed by designing a data architecture
model.
Materials & Methods: This systematic
review was conducted in 2020 through
searching five databases, including PubMed,
Web of Science, Science Direct, and Scopus
M, as well as Google scholar search engine
to identify metrics for COVID-19 data
architecture designing. Moreover, the search
formula definition, implication of inclusion
and exclusion criteria, search filtering
adjustment, and related study identification
were performed in this study. Subsequently,
the cases identifying the architecture data of
the COVID-19 component were
systematically extracted and categorized in
suitable classes. Finally, the management
system of the architecture model of the
patient was visualized in this study.
Ethics code:
IR.ABADANUMS.REC.1399.065
Findings: Out of 398 identified studies, 27
articles met the inclusion criteria. The
obtained data were categorized into five
classes, including organizations involved in
data management (data producer, data users,
and decision-makers), data sources,
information requirements (11 information
classes and 77 data elements), standards
(semantic and syntactic), and control quality
criteria of the data.
Discussions & Conclusions:
Implementation of customized data
architecture for COVID-19 can increase the
potential of the health care systems to
prevent the high prevalence of this disease
and improve the quality of care through
timely and effective health monitoring,
accurate epidemiological investigations,
clinical decision supports, and health-care
policymaking.
gastrointestinal cancers among human beings and the most important cause of death
in the world. Based on the risk of colorectal cancer for individuals, using an
appropriate screening program can help to prevent the disease. Therefore, the
purpose of this study was to design a model for screening colorectal cancer based
on risk factors to increase the survival rate of the disease on the one hand and to
reduce the mortality rate on the other.
Materials and Methods: By reviewing articles and patients' records, 38 risk factors
were detected. To determine the most important risk factors clinically, CVR(content
validity ratio) was used; and considering the collected data, Spearman correlation
coefficient and logistic regression analysis were applied for statistical analyses.
Then, four algorithms -- J-48, J-RIP, PART and REP-Tree -- were used for data
mining and rule generation. Finally, the most common model was obtained based
on comparing the performance of the algorithms.
Results: After comparing the performance of algorithms, the J-48 algorithm with
an F-Measure of 0.889 was found to be better than the others.
Conclusion: The results of evaluating J-48 data mining algorithm performance
showed that this algorithm could be considered as the most appropriate model for
colorectal cancer risk prediction.
reportable diseases are challenging because
these diseases include a large spectrum of
infectious conditions that need accurate,
precise, and timely reporting. To deal with
this problem, an integrated surveillance
system using the set of core data architecture
principles is crucial to ensure the effective
management of data. Therefore, this study
aimed to identify the core data architecture
requirements for effective management of
Coronavirus Disease 2019 (COVID-19),
followed by designing a data architecture
model.
Materials & Methods: This systematic
review was conducted in 2020 through
searching five databases, including PubMed,
Web of Science, Science Direct, and Scopus
M, as well as Google scholar search engine
to identify metrics for COVID-19 data
architecture designing. Moreover, the search
formula definition, implication of inclusion
and exclusion criteria, search filtering
adjustment, and related study identification
were performed in this study. Subsequently,
the cases identifying the architecture data of
the COVID-19 component were
systematically extracted and categorized in
suitable classes. Finally, the management
system of the architecture model of the
patient was visualized in this study.
Ethics code:
IR.ABADANUMS.REC.1399.065
Findings: Out of 398 identified studies, 27
articles met the inclusion criteria. The
obtained data were categorized into five
classes, including organizations involved in
data management (data producer, data users,
and decision-makers), data sources,
information requirements (11 information
classes and 77 data elements), standards
(semantic and syntactic), and control quality
criteria of the data.
Discussions & Conclusions:
Implementation of customized data
architecture for COVID-19 can increase the
potential of the health care systems to
prevent the high prevalence of this disease
and improve the quality of care through
timely and effective health monitoring,
accurate epidemiological investigations,
clinical decision supports, and health-care
policymaking.
gastrointestinal cancers among human beings and the most important cause of death
in the world. Based on the risk of colorectal cancer for individuals, using an
appropriate screening program can help to prevent the disease. Therefore, the
purpose of this study was to design a model for screening colorectal cancer based
on risk factors to increase the survival rate of the disease on the one hand and to
reduce the mortality rate on the other.
Materials and Methods: By reviewing articles and patients' records, 38 risk factors
were detected. To determine the most important risk factors clinically, CVR(content
validity ratio) was used; and considering the collected data, Spearman correlation
coefficient and logistic regression analysis were applied for statistical analyses.
Then, four algorithms -- J-48, J-RIP, PART and REP-Tree -- were used for data
mining and rule generation. Finally, the most common model was obtained based
on comparing the performance of the algorithms.
Results: After comparing the performance of algorithms, the J-48 algorithm with
an F-Measure of 0.889 was found to be better than the others.
Conclusion: The results of evaluating J-48 data mining algorithm performance
showed that this algorithm could be considered as the most appropriate model for
colorectal cancer risk prediction.