A Transparent Fuzzy Rule-Based Clinical Decision Support System for Heart Disease Diagnosis

Heart disease (HD) is a serious disease and its diagnosis at early stage remains a challenging task. A well-designed clinical decision support system (CDSS), however, that provides accurate and understandable decisions would effectively help the physician in making an early and appropriate diagnosis. In this study, a CDSS for HD diagnosis is proposed based on a genetic-fuzzy approach that considers both the transparency and accuracy of the system. Multi-objective genetic algorithm is applied to search for a small number of transparent fuzzy rules with high classification accuracy. The final fuzzy rules are formatted to be structured, informative and readable decisions that can be easily checked and understood by the physician. Furthermore, an Ensemble Classifier Strategy (ECS) is presented in order to enhance the diagnosis ability of our CDSS by supporting its decision, in the uncertain cases, by other well-known classifiers. The results show that the proposed method is able to offer humanly understandable rules with performance comparable to other benchmark classification methods.