A Proposed Model for Diabetes Diagnosis Using a Hybrid Approach Based on an Improved Harris Hawks Algorithm and Machine Learning Methods - Khani, Fatemeh; Ayat, Saeed; Esmaeili, Mahdi (Management Strategies and Engineering Sciences, 2026)
Early detection of diabetes represents a major challenge in healthcare systems, as a considerable proportion of patients are identified only at advanced stages, when opportunities for effective intervention have already diminished. Enhancing the accuracy of machine learning models depends critically on selecting optimal features—a process that becomes particularly important in high dimensional medical datasets. In the present study, an improved version of the Harris Hawks Optimization algorithm, referred to as Improved Harris Hawks Optimization (IHHO), was employed for feature selection. The proposed enhancements include the incorporation of chaotic maps for initialization, modification of the prey energy function, and the use of Lévy flight to strengthen the balance between exploration and exploitation. The performance of IHHO, in combination with eight widely used machine learning classifiers—RF, SVM, ANN, Logistic Regression, Linear Regression, KNN, Naive Bayes, and Decision Tree—was evaluated using the Pima Indians Diabetes dataset. The findings indicate that IHHO substantially outperforms both the Baseline methods and the standard HHO algorithm, achieving its best results in the IHHO+RF combination, with an Accuracy of 97.20 %, Precision of 94.51 %, Recall of 98.33 %, and an F1 Score of 96.38 %. Statistical analyses, including ANOVA, Post hoc tests, and the Friedman test, further confirm the significance of these differences. The results demonstrate that IHHO can serve as a robust framework for feature selection in medical diagnosis and can contribute to the development of reliable intelligent systems for diabetes screening.