Early Stage Diabetes Risk Prediction

Diabetes is a chronic condition that occurs when the body is unable to effectively use insulinor when the pancreas is unable to produce sufficient amounts of the hormone required to regulate bloodsugar levels. Conventional diagnostic methods, such as blood and urine glucose tests and family historyassessments, are commonly used by endocrinologists to detect diabetes. However, delayed detection remainsa significant concern as it increases the risk of severe complications over time. Therefore, early diabetesprediction is essential to improve patient outcomes and mitigate potential health risks.This study evaluates the performance of clustering techniques K-Means, Complete-Linkage, Expectation-Maximization, and Hierarchical K-Means for early-stage diabetes prediction. The impact of feature selec-tion using the Ant Colony Optimization (ACO) algorithm is analyzed to enhance clustering performance.The experiments are carried out using a data set comprising key diabetes risk indicators. The data setis preprocessed and the models are assessed using precision, recall, Rand index, and Fowlkes-Mallowsscore. The results indicate that ACO feature selection significantly improves the accuracy of the clustering,with Expectation-Maximization achieving the highest recall increase of 81.45% and Hierarchical K-Meansimproving the recall by 64. 93%. Precision scores for K-Means, Expectation-Maximization, and Hierar-chical K-Means reached 97% with ACO, while Complete-Linkage exhibited reduced effectiveness, withrecall dropping by 38.24%. False negative rates decreased significantly for Expectation-Maximization (-101instances) and Hierarchical K-Means (-87 instances) after feature selection. These findings demonstrate thatintegrating ACO with clustering methods enhances early diabetes prediction, providing a reliable approachfor clinical decision-making.