Ogar Michael Oko

Comparative Evaluation of Machine Learning Classifiers for Explainable Heart Disease Prediction Using the UCI Cleveland Dataset: A Decision Tree-centred Framework Integrating SHAP-based Clinical Interpretability Assessment and Deployable Clinical Decision Support System Development

A comparative evaluation of six supervised machine learning classifiers for heart disease prediction using the Cleveland Heart Disease Dataset indicates that interpretable machine learning models can achieve predictive performance comparable to or exceeding more complex algorithms while maintaining transparency and cli...