Classification of coronary heart disease using the multi-layer perceptron neural networks

Abstract

Coronary heart disease (CHD) is one of the leading causes of death worldwide. The complexity of risk factors such as blood pressure, cholesterol, smoking history, and unhealthy lifestyles often makes the diagnosis process less effective. With the increasing need for fast and accurate heart disease prediction systems, the use of artificial intelligence-based methods such as Neural Networks is a promising solution. This study aims to evaluate the ability of the Multi-Layer Perceptron (MLP) algorithm to classify CHD risk using the Framingham Heart Study dataset, while comparing it with other commonly used classification methods. This research used the collection of Framingham heart disease data containing 15 medical features. The data was then processed through cleaning, normalization, and class balancing using the SMOTE method. An MLP model was designed with two hidden layers using 200 and 128 neuron architectures, and tested in three training and testing data split scenarios (70:30, 75:25, and 80:20). The model was trained for 100 epochs and evaluated using accuracy, precision, and recall metrics to assess its classification performance. The experiment results show that MLP is able to produce high performance with 86.20% accuracy. 84.40% precision, and 88.56% recall. Compared to other methods such as Decision Tree and SVM, the experiment results show that MLP demonstrated superior classification accuracy. Thus, MLP has the potential to be an effective tool for supporting early diagnosis of coronary heart disease more intelligently and efficiently