The Multilayer perceptron model trained by the Multi-verse optimizer was the most appropriate model to classify coronary artery disease patients compared to nine other supervised learning methods.
Does an MVO-trained MLP model improve CAD detection accuracy compared to other supervised learning techniques?
An MLP model trained by MVO outperforms nine other supervised learning methods in predicting CAD using the Z-Alizadeh sani dataset.
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.
Jalali et al. (Tue,) conducted a other in Coronary artery disease. Multi-verse optimizer (MVO) based on Multilayer perceptron (MLP) training vs. Nine state-of-the-art supervised learning techniques was evaluated on Classification rates (accuracy and area under the curve). The Multilayer perceptron model trained by the Multi-verse optimizer was the most appropriate model to classify coronary artery disease patients compared to nine other supervised learning methods.