Medical Journal of the Islamic Republic Of Iran

en Assessment of the Performances of Adaptive Neuro-Fuzzy Inference System and Two Statistical Methods for Diagnosing Coronary Artery Disease Cardiovascular Surgery Cardiovascular Surgery Original Research Original Research Background: The accurate diagnosis of cardiac disease is vital in managing patients’ health. Data mining and machine learning techniques play an important role in the diagnosis of heart disease. We aimed to examine the diagnostic performances of an adaptive neuro-fuzzy inference system (ANFIS) for predicting coronary artery disease and compare this with two statistical methods: flexible discriminant analysis (FDA) and logistic regression (LR).    Methods: The data of this study is the result of descriptive-analytical research from the study of Mashhad. We used ANFIS, LR, and FDA to predict coronary artery disease. A total of 7385 subjects were recruited as part of the Mashhad Stroke and Heart Atherosclerotic Disorders (MASHAD) cohort study. The data set contained demographic, serum biochemical parameters, anthropometric, and many other variables. To evaluate the ability of trained ANFIS, LR, and FDA models to diagnose coronary artery disease, we used the Hold-Out method.For analyzing data, we used SPSS v25, R 4.0.4, and MATLAB 2018 software.     Results: The accuracy, sensitivity, specificity, Mean squared error (MSE)&lrm; , and area under the roc curve (AUC) for ANFIS were 83.4%, 80%, 86%, 0.166 and 83.4%. The corresponding values based on the LR method were 72.4%, 74%, 70% , 0.175 and 81.5% and for the FDA method, these measurements were 77.7%, 74%, 81%, 0.223, and 77.6%, respectively.    Conclusion: There was a significant difference between the accuracy of these three methods. The present findings showed that ANFIS was the most accurate method for diagnosing coronary artery disease compared with LR and FDA methods. Thus, it could be a helpful tool to aid medical decision-making for the diagnosis of coronary artery disease.   Adaptive Neuro-Fuzzy Inference System, Logistic Regression, Flexible Discriminant Analysis, Coronary Artery Disease 392 398 http://mjiri.iums.ac.ir/browse.php?a_code=A-10-7440-1&slc_lang=en&sid=1 Zahra Rajabzadeh RajabzadehZ951@mums.ac.ir 200319475328460077267 200319475328460077267 No Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran Nooshin Akbari Sharak noooshin_akbari@yahoo.com 200319475328460077268 200319475328460077268 No Student Research Committee, Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran Habibollah Esmaeili EsmailyH@mums.ac.ir 200319475328460077269 200319475328460077269 No Rheumatic Diseases Research Center, Mashhad University of Medical Sciences, Mashhad, Iran Majid Ghayour-Mobarhan ghayourm@mums.ac.ir 200319475328460077270 200319475328460077270 No Metabolic Syndrome Research Center, School of Medicine, & International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran Mohammad Taghi Shakeri ShakeriMT@mums.ac.ir 200319475328460077271 200319475328460077271 Yes Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran Zahra Pasdar zahrap10@gmail.com 200319475328460077272 200319475328460077272 No Institute of Applied Health Sciences, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen AB25 2ZD, UK