Using a Hybrid Model of Machine LearningAlgorithms for Efficient Cardiovascular illness Prediction

Abstract

Researchers have paid more attention to the field of medicine. Researchers have found several kinds of factors which leads to human early mortality. According to the relevant studies, illnesses are brought on by a variety of factors and heart-related illnesses is one of them. Numerous scholars suggested unconventional ways to prolong human life and aid medical professionals in the diagnosis, treatment and management of cardiac disease. Some practical techniques help the expert make a choice, but every effective plan contains some drawbacks. The suggested techniques in this paper examines an act of Decision Tree, Random Forest, XGBoost and Hybrid Model. Based on the results, we created a hybrid approach to archive data with more precision.