Comparative of machine learning methods for detecting cardiovascular disease

Cardiovascular disease (CVD) is a leading cause of death globally, making its early diagnosis and classification critical in healthcare management due to its high prevalence. The goal of the study is to explore the effectiveness of various machine learning algorithms in the early diagnosis and classification of cardiovascular disease, focusing on their performance and interpretability, to be used as an accurate screening model for CVD. A stratified cross-validation methodology has been employed to assess the performance of several machine learning algorithms rigorously. The analysis included both simpler models and more complex decision tree-based algorithms. The study revealed significant performance disparities among the algorithms. Simpler models like Naive Bayes and One Rule approached an acceptable 90% accuracy threshold. However, decision tree-based algorithms, particularly Logistic Models Trees (LMT), demonstrated the highest predictive performance and strong stability across different data subsets. LMT, integrating decision trees with logistic regressions, achieved the highest predictive performance among the evaluated algorithms. The high predictive performance and interpretability of LMT suggest that it may be a promising model for supporting clinical decision-making in CVD screening. The study advocates for the strategic selection of decision tree-based algorithms to enhance diagnostic precision and patient outcomes in CVD. Highlighting the superior performance and interpretability of these models underlines the importance of thoughtful algorithm selection in the fight against one of the foremost global health challenges.