Abstract

- One of the biggest causes of death in the globe is heart disease. Accurate cardiac disease prediction can aid in early detection and prevention. Heart disease has been accurately predicted using machine learning models. In this study, we investigated 6 machine learning models for predicting heart disease [1]. To train and assess these models, we used the Cleveland HeartDisease dataset, a publically accessible dataset. Age, sex, the type of chest discomfort, blood pressure, cholesterol levels, and other characteristics were among the features used to train the models. To determine the most reliable model for heart disease prediction, the outcomesfrom various models' tests were compared.

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