Enhanced accuracy for heart disease prediction using artificial neural network

Abstract

Making an accurate and timely diagnosis of cardiac disease is critical for preventing and treating heart failure. The accuracy of results produced by traditional machine learning (ML) algorithms is satisfactory. On the other hand, deep learning algorithms result in higher prediction accuracy. In this study, we used an artificial neural network (ANN) model to construct a deep learning diagnosis system for heart disease prediction. The developed ANN prediction model achieved 93.44% accuracy, which is 7.5% higher than a traditional ML model support vector machine (SVM). Additionally, using a simpler neural network reduced the time taken for training and classification to less than a minute.