An efficient neuroevolution approach for heart disease detection

© 2019 IEEE. Cardiovascular diseases are one of the main causes of death among individuals over the last decade. Early diagnosis and recognizing of warning signs of this disease facilitate medical treatment for patients. Angiography is considered a reliable tool to diagnose coronary artery disease (CAD), however, it has some demerits such as complications and costs. Data mining techniques are considered as reliable and powerful tools for early diagnosis of diseases and are widely used in the medicine filed for recent years. In this paper, we use these techniques for early detection of CAD by applying them on a well-known CAD dataset named Z-Alizadeh sani. Thus, an effective nature-inspired optimization algorithm named Multi-verse optimizer (MVO) based on Multilayer perceptron (MLP) training as well as nine states of the art supervised learning techniques are employed for CAD prediction. As this dataset has 54 features, before applying the supervised learning algorithms, we used a feature selection method to identify the most effective features. This procedure enhances the prediction capability of the utilized algorithms. The classification rates of all algorithms are compared with each other using the most usable evaluation metrics including accuracy and area under the curve. Eventually, the experimental results show that the most appropriate model to classify CAD patients is the MLP model trained by MVO among all other nine supervised learning methods.