Heart disease prediction using machine learning

Abstract

Today, heart disease is a severe hazard to one's health that may result in death or a long-term impairment because of the complicated blend of clinical and pathological evidence required to diagnose it. Despite the fact that medical diagnosis is a complex activity that plays a critical role in saving human lives, there is a dearth of appropriate tools to detect hidden linkages and patterns in electronic health data. Computer-based automated decision support systems are needed to lower the costs of clinical testing because of this complexity. " In this study, we provide a method for predicting the presence of cardiac disease based on clinical data collected from patients. In this study, the primary goal is to develop a predictive model for heart disease based on a combination of characteristics (risk factors). Different machine learning classification strategies will be deployed and evaluated based on conventional performance metrics such as accuracy in order to compare different machine learning algorithms.