Enhancing Early Diagnosis of Heart Disease: A Comparative Study of K-NN and Naive Bayes Classifiers Using the UCI Heart Disease Dataset - Permana, Angga Aditya; Arsanah, Arsanah (Journal of Intelligent Computing & Health Informatics, 2024)
Heart disease remains a leading cause of mortality globally, necessitating accurate predictive models for early detection and intervention. This study conducted a detailed comparative analysis of the K-nearest neighbor (KNN) and naive bayes classifiers using the UCI Repository Heart Disease dataset to determine the most effective algorithm for heart disease prediction. Our results demonstrate that the proposed KNN outperforms naive bayes in terms of several key metrics: KNN achieved an accuracy of 91.25%, which surpasses naive bayes' accuracy of 88.75%. Additionally, KNN exhibited superior precision (92%), recall (90%), and an F1 score (91%) compared to naive bayes, which demonstrated precision of 89%, recall of 87%, and an F1 score of 88%. The findings of this study have substantial practical implications for medical data analysis. The high accuracy and reliability of the KNN algorithm make it a valuable tool for healthcare professionals in the early diagnosis of heart disease. Implementing KNN-based predictive models can enhance patient outcomes by timely and accurate detection of heart disease, facilitating early intervention, and reducing the risk of severe cardiac events. Moreover, the user-friendly interface of the proposed system streamlines the classification process, making it accessible for clinical use. Future research should explore the integration of additional machine learning algorithms and ensemble methods to further improve prediction accuracy. Developing real-time prediction systems integrated with electronic health records (EHR) could revolutionize patient monitoring and proactive healthcare management, ultimately contributing to better patient outcomes and more efficient healthcare delivery.