Performance Comparison of Decision Tree and Support Vector Machine Algorithms for Heart Failure Prediction

Abstract

Heart failure is a significant global cause of mortality and morbidity. This research article aims to evaluate the performance of decision tree (DTree) and support vector machine (SVM) methods in predicting heart disease. The study utilizes a dataset with diverse features and employs exploratory data analysis (EDA), clustering, and classification techniques to gain insights and evaluate the performance of the two methods. The results demonstrate the effectiveness of both DTree and SVM in predicting heart disease. Notably, SVM outperforms DTree in terms of accuracy, precision, recall, and F1-score. However, the performance of these methods is influenced by the preprocessing steps applied, indicating the importance of selecting appropriate data preprocessing techniques for optimal performance with specific machine learning algorithms. This study emphasizes the potential of machine learning algorithms in predicting heart disease and underscores the significance of thoughtful preprocessing technique selection to enhance performance.