A COMPARATIVE STUDY FOR THE PREDICTION OF HEART ATTACK RISK AND ASSOCIATED FACTORS USING MLP AND RBF NEURAL NETWORKS

Abstract

Abstract— Aim: The aim of this study is to develop a predictive classification model that can identify risk factors for heart attack disease. Materials and Methods: In the study, patients with low and high probability of having a heart attack were examined. Variable importance was calculated to identify risk factors. The radial basis function and multilayer perception neural networks were used to compare the classification prediction results. Results: MLP model criteria; Accuracy 0.911, F1 score 0.918, Specificity 0.92, Sensitivity 0.903, while RBF model criteria were obtained as accuracy 0.797, F1 score 0.812, Specificity 0.84, Sensitivity 0.765. The first three most important factors that may be associated with having a heart attack were obtained as trestbps, oldpeak, and chol. Conclusion: According to the prediction results of the heart attack, it can be said that the model created with the MLP neural network has more successful predictions than the model created with the RBF neural network. In addition, estimating the importance values of the factors most associated with heart attack (obtaining the most important biomarkers that may cause heart attack) is a promising result for the diagnosis, treatment and prognosis of the disease. Keywords— Heart Attack, machine learning, neural networks, classification, variable importance.

Keywords

• Heart Attack
• machine learning
• neural networks
• classification
• variable importance

References

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