Anagu Emmanuel John

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

Cardiovascular disease remains one of the leading causes of mortality worldwide, necessitating the development of accurate and interpretable predictive systems that support early diagnosis and clinical decision-making. While numerous machine learning models have demonstrated promising predictive capabilities, many oper...