Date of Publication :2nd May 2023

Abstract: One of the leading causes of death in the modern world is heart disease. The terms "heart disease" and "cardiovascular disease" are frequently used interchangeably. Heart attacks, chest pain (angina), strokes, and other illnesses caused by restricted or obstructed blood vessels are together referred to as cardiovascular disease. Clinical data analysis faces a significant problem when predicting cardiovascular disease. The main challenge in today's healthcare is provision of best quality services and effective accurate diagnosis. A machine learning model can be very helpful in the early detection and providing treatment for people with cardiovascular disease or who are at high cardiovascular risk. In this paper we have developed a Machine Learning Model with Random Forest Algorithm to detect Heart Disease accurately. We have also compared Random Forest with Logistic Regression and K-Nearest Neighbors algorithm and our experimental results shown that the accuracy parameter is high in Random Forest based heart disease detection and low in K Nearest Neighbor approach.

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