An Interpretable Clinical Expert System for Heart Disease Diagnosis via Decision Tree-Derived Rules

Cardiovascular diseases remain the leading cause of global mortality, responsible for an estimated 17.9 million deaths annually. Early and interpretable diagnosis is critical to improving patient outcomes. This study bridges the gap between black-box machine learning models and clinical interpretability by developing a hybrid expert system that derives transparent, evidence-based diagnostic rules directly from patient data. We trained a constrained Decision Tree model (maximum depth = 3) on a publicly available dataset of 918 patients to ensure inherent explainability. The tree structure was systematically parsed and translated into a set of eight mutually exclusive IF-THEN clinicalrules, forming the knowledge base of a prototype rule-based expert system. Each rule is accompanied by its confidence score, support, and a clinically meaningful interpretation. The system achieved an accuracy of 82.1% (95% CI: 76.5 – 87.6%), a precision of 87.1%, and a recall of 79.4% on an independent test set. Five-fold cross-validation confirmed robustness (mean accuracy: 81.5% ± 6.1%), and the area under the ROC curve (AUC) was 0.886, indicating excellent discriminative ability. Crucially, for each case, the system outputs the specific diagnostic rule applied, a risk-stratified recommendation (e.g., urgent cardiology consultation, routine screening), and a confidence score. and a clinical interpretation, moving beyond a mere prediction to an auditable diagnostic aid. This transforms the model from a predictive endpoint into a fully auditable and interpretable clinical decision support tool. This work provides a reproducible and scalable framework for building accurate, transparent, and clinically deployable diagnostic systems, facilitating greater trust and adoption in real-world healthcare settings.