Heart Disease Prediction using Data Mining Classification Algorithms

Heart diseases, also referred to as "cardiovascular diseases," are a group of disorders that affect the heart. This illness can cause a heart attack, stroke, and other symptoms. After examining a few research papers on the subject, it became clear that the majority of them used a single machine learning algorithm to predict heart disease. A few of them state that they are unable to enhance their model's performance through optimization techniques. As a result of these findings, they encountered some difficulties in effectively predicting heart disease using their suggested method. In an earlier study PCA was also used, but it failed to provide considerable accuracy for such a sensitive research area, i.e., medical diagnosis. Data for this method was gathered from the "Heart Disease UCI" UCI repository, which was accessible on Kaggle. Working upon the given dataset we used various dimensionality reduction techniques, using various classifiers and found out their effectiveness. Thus, we were able to get considerably higher accuracy (98%) by using certain techniques to de-noise data (checking correlations, outliers, removing them etc.), using the MLP classifier.