Heart beat level prediction usingMachine Learning Algorithms

Abstract

Heart disease is one of the leading causes of death in the world today. Cardiovascular disease prediction is a critical challenge in clinical data analysis. Machine learning (ML) has been shown to be effective in assisting with decision-making and prediction from the vast amounts of data generated by the healthcare industry. Many lives can be saved if heart disease is detected early. Machine learning classification techniques have the potential to significantly benefit the medical field by providing accurate, unambiguous, and rapiddiseasediagnosis. a result, both doctors and patients should set aside time for prediction. This paper proposes a model to compare the accuracies of applying rules to the individual results of support vector machine, decision trees, logistic regression, and Random forest on the Cleveland Heart Disease Database in order to present an accurate model of predicting heart disease