COMPARISON OF MACHINE LEARNING ALGORITHMS FOR HEART DISEASE PREDICTION

Abstract

Machine learning, one of the most well-known applications of artificial intelligence, is altering the world of research. The aim of this study is to generate predictions for Heart Disease Prediction (HDP) by employing effective machine learning approaches and to predict whether an individual has heart disease. The primary objective is to evaluate the predictive accuracy of various machine learning algorithms in predicting the presence or absence of heart disease. The KNIME data analysis program has been selected, and overall accuracy is chosen as the primary indicator to assess the effectiveness of these strategies. Utilizing details such as chest pain, cholesterol levels, age, and other factors, along with different machine learning technologies such as K Nearest Neighbor (KNN), Naive Bayes, and Logistic Regression, a dataset of 319,796 patient records with 18 attributes was utilized. Naive Bayes, K Nearest Neighbor (KNN), and Logistic Regression were employed as machine learning techniques, and their prediction accuracies were compared. The application results indicate that the logistic regression approach outperforms the K Nearest Neighbor method and the Naive Bayes method in terms of predicting accuracy for heart disease. The prediction accuracy of K-NN is 90.77%, Naive Bayes is 86.633%, and logistic regression is 91.60%. In conclusion, machine learning algorithms can accurately identify heart disease. The results suggest that these methods could assist doctors and heart surgeons in determining the likelihood of a heart attack in a patient.