Abstract

In today's world, accurately estimating the risk of heart disease in patients is a very critical problem. It's also crucial to detect the risk of heart disease as it reduces death tolls. If the heart disease risk is predicted in its early stage, then with proper medication, the death toll caused by it can be reduced. Machine learning (ML) here plays a key role in helping the doctor throughout the prediction and detection phase of heart disease(HD) by catering to a stronger bias for decision making and prediction based on the patient’s dataset provided by hospitals. This paper's primary goal is to develop a suitable, precise machine-learning model that can anticipate HD at an early stage. A diversity of machine learning techniques has been proposed for finding the most accurate method and feature subset. Different classification algorithms have been utilised, including logistic regression, random forest algorithm, gradient boosting, support vector machines (SVM), KNN, and decision trees (DT). An accurate model for predicting HD is obtained by employing several cross-validation approaches and using both public and private datasets. Applying the random forest classification approach to the combined dataset yields the best outcomes, with an accuracy of 85.25%.

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