Optimizing PROAFTN classifier with ant colony algorithm: Enhanced diabetes detection benchmarking. Modelling in Java

The increasing global prevalence of diabetes highlights the need for accurate diagnostic tools to improve early detection and effective treatment planning. Traditional classification models often struggle to achieve optimal performance due to limitations in parameter tuning and adaptability to complex datasets. To address these limitations, this article introduces PROAnt, an innovative learning approach that enhances the robustness and efficiency of the PROAFTN multicriteria classification method. PROAnt leverages the computational power of Ant Colony Optimization (ACO) to dynamically fine-tune key parameters, such as intervals and weights, at the core of the PROAFTN classification process. This learning methodology is crucial because PROAFTN depends on these parameters for classification. ACO, inspired by the foraging behaviour of ants, achieves rapid and accurate convergence with a minimal number of parameters, outperforming many traditional optimization techniques. This study demonstrates that ACO can inductively infer PROAFTN’s parameters from data, achieving high accuracy and precision. Evaluations were conducted on a publicly available diabetes dataset containing 100,000 samples, sourced from Kaggle’s 2023 machine learning competition. This dataset includes demographic and clinical features, providing a robust basis for model benchmarking. The model achieved a classification accuracy of 98.42\%,