A Deep Learning Technique based on Generative Adversarial Network for Heart Disease Prediction

Authors

Chour Singh Rajpoot
Research scholar, Manipal University, Jaipur, India

Dr. Praveen Gupta
Professor, computer engineering, Jaipur India

Abstract

Globally, heart illnesses are the leading cause of death. A lot of knowledge and expertise is needed to accurately predict the condition. Although the sickness can be foreseen, it takes a long time to cure. This can help patients prevent heart attacks, as well as medical physicians discover the key causes of heart attacks and evade them before they occur. As a result, the rate of death can be reduced by initiating treatment at an early stage. A fast-growing technique, Data Mining (DM) and artificial intelligence (AI) techniques are used to acquire substantial data and forecast the outcome. A generative adversarial network (GAN) is used to forecast the chance of cardiovascular illness in a patient. Using GAN for detection of heart disease early, this technique is worried with temporal data modelling. We associated the results with existing approaches and got good outcomes. In terms of performance evaluation measures, the suggested method outperforms the existing methodologies. The proposed approach achieves an accuracy of about 98.5%.