Prediction of Patients with Heart Disease using Artificial Neural Network and Adaptive Boosting techniques

Machine learning (ML) technique behind the most existing abilities fields in several areas like languages processing, robotics, including medicine. The most important medical applications are 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. Here, a supervised machine learning medical diagnosis support system (MDSS) for atherosclerosis prediction is presented that is able to obtain and learn automatically knowledge from each patient's clinical data. Therefore, we used the various ML classifiers for the proposed medical diagnosis support system for atherosclerosis. Two supervised ML algorithms (Artificial Neural Network and Adaptive Boosting) were used in order to compare which one is more efficient for atherosclerosis diagnosis. 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 Recall, Accuracy and Precision. Furthermore and F1 score measures were also calculated to greatly increase the proposed system performance. Consequently, the proposed model can be used to support healthcare and facilitate large-scale clinical diagnostic of atherosclerosis diseases.