Simulation and Analysis of Heart Disease Predictor Using Machine Learning Techniques

Heart disease is easier to treat and has a lower fatality rate when detected early. Doctor visits, electrocardiograms, cardiac MRIs, and several medical procedures, such as stress tests for people, are all traditional means of predicting heart disease. This manual detection often takes a long time and requires much professional input during patient visits. The heart transmits to the patient all minor symptoms that the patient can only make sense of based on knowledge throughout the usual course of an individual's conduct. In this paper, an evaluation of various machine learning algorithms, such as Logistic Regression, K-Nearest Neighbor, Random Forest Classifier, Decision Tree, and Naive Bayes, has been conducted to predict heart disease. Their performance is assessed based on accuracy, recall, precision, and F1-score metrics. The simulation results have been obtained using Python programming software. The simulation's results show that, out of all the classification algorithms taken into consideration, the random forest technique had the best accuracy, coming in at 88%.