PERFORMANCE ANALYSIS OF CLASSIFICATION MODELS FOR MEDICAL DIAGNOSTIC DECISION SUPPORT SYSTEMS

Abstract

As a part of electronic healthcare systems, medical diagnostic decision support systems have been more popular in clinical routine. It is critical to decide the best model to provide reliable machine learning based decision support in diagnostic problems. In this study, the performance of common classification algorithms have been comparatively evaluated using public medical datasets. The experimental results reveal that, although there is no single best algorithm for all datasets, MLP and Naive Bayes methods have provided relatively higher success rates.