Comparative Analysis of Machine Learning Algorithms for Heart Disease Prediction

Abstract

One of the more difficult ailments is heart disease, which affected a large number of people worldwide. Heart illness must be promptly and accurately diagnosed in order to be treated, especially in the field of cardiology. In this work, we suggested a machine learning-based approach for diagnosing cardiac disease that is both effective and accurate. The system was created using classification algorithms, which Standard feature selection algorithms like Relief, Minimal redundancy maximal relevance, Least absolute shrinkage selection operator, and Local learning have been used to omit unnecessary and redundant features. Other feature selection algorithms include Support vector machine, Logistic regression, Artificial neural network, K nearest neighbor, Nave bays, and Decision tree. Additionally, we provided a brand-new, quick conditional mutual information feature selection approach to address.