Hybrid Model Feature Selection with the Bee Swarm Optimization Method and Q-Learning on the Diagnosis of Coronary Heart Disease

Coronary heart disease is a type of cardiovascular disease characterized by atherosclerotic plaque, which causes myocardial infarction or sudden cardiac death. Since this sudden heart attack has no apparent symptoms, the early detection of the risk factors for coronary heart disease is required. Many studies have been conducted to diagnose heart disease, including studies that tested various classifiers, feature selection and detection models on several coronary heart disease datasets. As a result, this research aims to learn about the effect of the bee swarm optimization algorithm combined with Q-learning for optimizing the feature selection in improving the prediction of heart disease. This detection model was tested against various classification methods and evaluated against multiple performance measures, such as accuracy, precision, recall and the area under curve (AUC), to identify the best model for heart disease prediction and the benefit of the medical community. The test results show that the proposed method outperforms the existing process regarding the feature selection.