Coronary Heart Disease Prediction Using Voting Classifier Ensemble Learning

In the medical profession, sometimes, it is very difficult to save a human being's life because a massive amount of harm has been already done to the critical organs of the patient. The main reason for this harm is either the disease was undetected or any major symptoms related to that particular disease were not possessed by the patient in an earlier stage. Therefore, the early detection of any deadly disease becomes very important as sometimes treatment might require a longer period than expected and the patient may have to bear lots of adversarial impacts. One such deadly disease that is currently impacting human life is Coronary Heart Disease. In the past few years, Machine Learning (ML) provide helped in the prediction, as well as in the treatment of various kinds of heart-related diseases. This research aims to make a better Coronary Heart Disease prediction model using Ensemble Learning Techniques. Therefore, the Ensemble Learning methods such as Hard Voting Classifier (HVS) and Soft Voting Classifier (SVC) are applied, and the highest accuracy of 83.2% and 82.5% are achieved respectively.