Heart Disease prediction using Random Forest Algorithm

Heart disease is one of the major health issues globally, for which proper prediction techniques arerecommended for proper intervention. This paper aims at optimizing the prediction accuracy of heartdisease using various machine learning models. Advanced data-mining techniques are essential herebecause the researchers are combining electronic health records and wearable medical devices to capturecontinuous data.The methodology involves deep data preprocessing techniques, missing value handling,and feature scaling techniques. The models provided are fine-tuned by experimentation andhyperparameter tuning for superior predictive performance. The research paper is focused on predictingwhich patients are likely to suffer from heart disease based on various medical attributes. A heart diseaseprediction system is prepared using some machine learning algorithms, such as logistic regression, topredict and classify patients with heart disease. The proposed model depicts good accuracy againstprevious classifiers, thereby alleviating the pressure on the probability of correctly identifying heartdiseases. It enhances medical care and decreases costs, thereby providing some valuable knowledge inpredicting patients having heart disease.