Clinical Decision Support System for Diabetes Classification with an Optimized CNN using PSO

Diabetes, a pervasive chronic metabolic disorder, affects a substantial portion of the global population. The timely and precise diagnosis of diabetes is pivotal for effective management and improved patient outcomes. In this study, we introduce an innovative approach aimed at augmenting the classification performance of Convolutional Neural Networks (CNNs) in diabetes diagnosis. Utilizing the Particle Swarm Optimization (PSO) algorithm, we fine-tune the CNN model to enhance the accuracy and efficiency of diabetes classification. Our comprehensive experiments, conducted on the Pima Indians Diabetes dataset, substantiate the effectiveness of our optimized CNN. These findings underscore the potential of integrating CNN and PSO optimization methodologies to significantly boost the accuracy of diabetes diagnosis, thereby facilitating more accurate assessments and tailored treatment strategies for patients. We evaluated the proposed model using standard metrics such as precision, recall, F1-score, and overall accuracy, with results demonstrating that the PSO-based optimized CNN model outperforms the custom CNN, achieving the highest precision, recall, F1-score, and overall accuracy.