SWARM OPTIMIZED MACHINE LEARNING MODEL FOR ENHANCED PREDICTION OF CORONARY ARTERY DISEASE

Coronary artery disease poses a critical health challenge requiring accurate and timely diagnosis. This research introduces an optimized classification framework that integrates Artificial Bee Colony (ABC) algorithm with Naive Bayes (NB) to strengthen prediction performance. The working mechanism begins with feature extraction from a benchmark coronary dataset, where ABC acts as a bio-inspired swarm intelligence method to identify the most relevant clinical attributes by mimicking the intelligent foraging behavior of bees. By eliminating redundant and noisy features, ABC enhances the learning environment for the Naive Bayes classifier. The probabilistic nature of NB then utilizes these refined features to compute posterior probabilities for classification, ensuring efficient decision boundaries. Experimental validation shows that the ABC-NB model achieves 93.45% accuracy, 94.23% sensitivity, and 92.56% specificity, outperforming standard classifiers in early-stage CAD detection. The model’s simplicity, interpretability, and high predictive value make it suitable for integration into clinical workflows. Its low computational complexity enables deployment on embedded medical systems. This framework promotes scalable, cost-effective, and accurate cardiac risk prediction. Future advancements will involve real-time prediction support, dynamic retraining with streaming patient data, and expansion to multi-modal datasets for comprehensive cardiovascular profiling across diverse populations and clinical scenarios.