Performance Evaluation of Machine Learning Techniques (MLT) for Heart Disease Prediction

Abstract

The leading cause of death worldwide today is heart disease (HD). The heart is recognised as the second-most significant organ behind the brain. A successful outcome of treatment can be improved by an early diagnosis which can significantly reduce the chance of death in health care. In this paper, we proposed a method to predict heart disease. We used various machine learning algorithms (MLA), namely, logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), Naive Bayes (NB), random forest (RF), and decision tree (DT). With the testing data set, we evaluated the model's accuracy in heart disease prediction. When compared to the other five models, the random forest and k-nearest neighbor approaches perform better. With a 99.04% accuracy rate, the k-nearest neighbor algorithm and random forest provide the best match to the data as compared to other algorithms. Six feature selection algorithms were used for the performance evaluation matrix. MCC parameters for accuracy, precision, recall, and F measure are used to evaluate models.