Machine Learning Based Heart Disease Prediction Using Feature Optimization Algorithm

Coronary artery disease is a leading cause of death globally. In order to lower the mortality rate, early detection and accurate prediction of cardiac disease are necessary. It requires experience and deeper knowledge to predict heart illness, which is a very difficult assignment even for medical practitioners. Predicting cardiovascular illnesses is a key function of new machine learning-based medical assistance technology. Machine learning is a method that relies on algorithms to analyze data, gain knowledge from that data, and then make decisions based on that knowledge. This research presented a novel hybrid model that combines elements of feature optimization, ensemble approach, and feature selection. This unique combination will help medical professionals take precautions against the accident by creating a better model that can predict cardiac problems more rapidly and accurately than current models.