OPTIMIZED CORONARY ARTERY DISEASE PROGNOSIS USING ARTIFICIAL BEE COLONY FEATURE SELECTION AND NAIVE BAYES CLASSIFICATION

International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

Abstract

Coronary artery disease poses a critical health challenge requiring accurate and timely diagnosis. According to the World Health Organization (WHO), approximately 18 million deaths occur yearly worldwide due to heart or cardiovascular disease. 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 eradicate redundant and noisy features, ABC enhances the learning environment for the Naive Bayes classifier. 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.

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Keywords

Coronary Artery Disease, Artificial Bee Colony, Naive Bayes, Feature Selection Medical Diagnosis, Swarm Intelligence.