Journal of Intelligent Systems and Internet of Things
JISIoT
2690-6791
2769-786X
10.54216/JISIoT
https://www.americaspg.com/journals/show/3692
2019
2019
Greylag Goose Optimization for Diabetes Prediction: Feature Selection Meets Advanced Machine Learning
Department of Computer, Systems Engineering, Faculty of Engineering at Shoubra, Benha University, Egypt
Gomaa
Gomaa
Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt; Applied Science Research Center. Applied Science Private University, Amman, Jordan; Jadara University Research Center, Jadara University, Jordan
El-Sayed M. El
El-kenawy
Department of Computer, Systems Engineering, Faculty of Engineering at Shoubra, Benha University, Egypt
Shady Y. El
El-Mashad
Diabetes mellitus remains a global health concern, necessitating both accurate and effective diagnostic methodologies. This condition presents a significant challenge due to the high dimensionality of clinical datasets and the inherent complexity of diabetes classification. To address this problem, this study integrates feature selection and machine learning architectures to enhance diabetes prediction accuracy. A novel framework based on the Binary Greylag Goose Optimization (bGGO) algorithm is proposed to optimize feature selection, thereby improving classification performance. A comprehensive evaluation uses multiple classifiers, including Decision Trees, k-nearest Neighbors, Support Vector Machines, Random Forests, and Multilayer Perceptron (MLP). The experimental results demonstrate that bGGO significantly enhances feature selection quality, improving classification metrics, particularly for MLP, which achieves the highest classification accuracy of 95.98%. These findings underscore the efficacy of combining metaheuristic optimization with machine learning for diabetes diagnosis, offering a scalable and interpretable approach for real-world healthcare applications. The proposed methodology contributes to more precise risk estimation and the development of individualized intervention strategies, facilitating early diagnosis and effective disease management.
2025
2025
13
25
10.54216/JISIoT.160202
https://www.americaspg.com/articleinfo/18/show/3692