Heart Disease Risk Assessment Using Bee Colony and Machine Learning Techniques

Abstract

Heart-related diseases (CVDs) continue to be a major worldwide health issue, requiring sophisticated techniques for precise risk assessment. This research

 a new method for assessing the risk of heart disease that combines a hybrid machine learning algorithm with bee colony optimization, or ABC. By optimizing feature selection and model parameters, the ABC algorithm raises the system's prediction accuracy. The utilization of bio-inspired optimization techniques in healthcare underscores the potential for innovative solutions in cardiovascular risk assessment. This research not only presents a novel approach to risk assessment but also contributes to the evolving landscape of personalized and effective strategies for early detection and prevention of heart diseases.