ARTIFICIAL BEE COLONY-DRIVEN FEATURE SELECTION EMPOWERING NAIVE BAYES CLASSIFIER FOR SUPERIOR CORONARY ARTERY DISEASE PROGNOSIS

The rise in mortality due to Coronary Artery Disease (CAD) underscores the urgent need for accurate, early-stage detection systems. Traditional classification models often struggle with imbalanced datasets, irrelevant features, and limited interpretability, hindering clinical reliability. This study introduces an optimized hybrid diagnostic model Firefly Swarm Optimization-based Decision Tree (FSO-DT)designed to overcome these limitations. The approach leverages the bio-inspired characteristics of firefly swarms to perform robust feature selection, reducing dimensionality and enhancing classification relevance. The refined feature set is subsequently processed through a decision tree classifier, selected for its transparency and clinical interpretability. The proposed model was evaluated using a benchmark CAD dataset, assessing performance through key metrics including accuracy, sensitivity, specificity, and F1-score. Results indicate significant improvements compared to conventional classifiers and unoptimized decision trees. This optimization not only improves classification performance but also ensures model efficiency by minimizing computational overhead. The FSO-DT model delivers an interpretable and scalable solution, capable of aiding clinicians in risk assessment and decision-making processes. The bio-inspired nature of the algorithm ensures adaptability across diverse clinical scenarios, making it a promising tool in medical data analytics. This work contributes to intelligent healthcare systems by integrating evolutionary computation with interpretable learning, fostering advancements in early diagnosis and patient-centered outcomes.