Comparative Analysis of Machine Learning, Decision Trees, and K-Nearest Neighbors for Heart Disease Prediction

Abstract. This study investigates the efficacy of Machine Learning (ML), Decision Trees, and K-Nearest Neighbors (KNN) techniques in predicting heart disease, aiming to identify their strengths and limitations. ML models are effective in detecting complex patterns and delivering evolving predictions from large datasets but require high-quality data and can be challenging to interpret. Decision Trees provide clear, understandable decision rules, which is beneficial in clinical settings, yet they are susceptible to overfitting and instability. KNN, valued for its simplicity and flexibility, classifies heart disease based on similarity but struggles with high computational costs and sensitivity to noisy data. Experimental results indicate that each model has distinct advantages: ML excels in pattern recognition, Decision Trees offer interpretability, and KNN handles diverse data effectively. However, each also faces challenges that impact performance, such as data quality issues for ML, overfitting for Decision Trees, and computational demands for KNN. The study suggests that balancing these strengths and weaknesses is crucial for optimizing heart disease prediction models. Future research should explore hybrid approaches that combine these models' advantages while addressing their respective limitations to improve predictive accuracy and practical application in real-world scenarios.