اسماعيل حيدري

Until recent years, researchers and practitioners in the healthcare field have had stumbling blocks in their effort to fully utilize and benefit from data. Data were difficult and expensive to obtain and filter. The hardships of data collection and storage were mostly eased with advancements in both medical and data analytics technologies. With an abundance of newly developed data and analytical tools, the question has now become which tools are more effective and appropriate for different medical applications. Using 5 different supervised machine learning classification algorithms: logistic regression, decision tree, random forest, K-nearest neighbor, and multilayer perceptron. The chosen datasets were of varying sizes, time periods, attributes, conditions, and sources in order to avoid drawing conclusions based on a single dataset. Each dataset was analyzed with the complete set of attributes and with a reduced set for performance comparison within the same algorithms. The model performance eva‎luation criteria are based on their prediction accuracy in terms of area under the receiver operating characteristic curve (AUC) score and their model training computational times. The comparative results revealed the superiority of random forest classifier, K-nearest neighbor, and multilayer perceptron in terms of prediction accuracy with computational time as the trade-off. Furthermore, the reduced models provided competitiveness in both prediction accuracy and computational time against their full model counterparts.

يادگيري ماشين، بيماري قلبي، رگرسيون لجستيك، درخت تصميم، جنگل تصادفي، k-نزديك ترين همسايگي و پرسپترون چندلايه

Machine learning, Heart disease, Logistic regression, Decision tree, Random forest, K-nearest neighbor, Multilayer perceptron

esmaeil heydari

Dr. Javad Vahidi/ Dr. Jalil Rashidinia