Heart Disease Prediction Using Support Vector Machine and Artificial Neural Network

Abstract

Heart-related illnesses, often known as cardiovascular diseases, have been the leading cause of mortality globally over the past several decades and are now recognized as the most major illness in both India and the rest of the globe. The severity out of the disease can be avoided with proper care at proper stage. This disease claims early and accurate prediction to avoid causalities. As proper medical support is not adequate, diseases are not being identified at the proper time and treatment cannot be started. Machine learning algorithms have shown promise in predicting heart disease risk based on patient data. In this study, a machine learning-based heart disease prediction model has been presented. The objective of the work is to build a machine learning-based model for early and adequate prediction of heart disease. The proposed model has utilized support vector machine and artificial intelligence with an accuracy of 81.6% and 86.6%, respectively. The findings show that the model predicts heart disease risk with excellent accuracy, sensitivity, and specificity, offering healthcare professionals a useful tool to pinpoint people who may be more at risk of developing heart disease.

Received: 6 March 2023 | Revised: 17 April 2023 | Accepted: 23 April 2023 

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.