Supervised Machine Learning Based Medical Diagnosis Support System for Prediction of Patients with Heart Disease

Abstract

Application in the field of medical development has always been one of the most important research areas. One of these medical applications is the early prediction system for heart diseases especially; coronary artery disease (CAD) also called atherosclerosis. The need for a medical diagnosis support system is to detect atherosclerosis at the earlier stages to optimize the diagnosis, avoid the advanced cases, and reduce treatment costs. Earlier, the datasets are collected from specific medical sources and have evaluated against computer applications. In this paper, a supervised machine learning medical diagnosis support system (MDSS) for atherosclerosis prediction is presented that able to obtain and learn automatically knowledge from each patient's clinical data. Therefore, we used three Machine Learning (ML) classifiers for the proposed MDSS for atherosclerosis. Thus, this work is accomplished using databases collected from the UCI repository (Cleveland, Hungarian) and Sani Z-Alizadeh dataset. The performance metrics were computed utilizing Accuracy, Recall and Precision. Furthermore, F1-score and Matthews's correlation coefficient these measures were also calculated to greatly increase the proposed system performance. Additionally, 10-fold cross-validation methods have been used for proposed model performance evaluation that achieved 94% as the best accuracy average. Consequently, the proposed model can be used to support healthcare and facilitate largescale clinical diagnostic of atherosclerosis diseases.

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