Volume 16 , Issue 2 , PP: 13-25, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Gomaa Mohamed Ismail 1 * , El-Sayed M. El-kenawy 2 , Shady Y. El-Mashad 3
Doi: https://doi.org/10.54216/JISIoT.160202
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.
Diabetes , Meta-heuristic Optimization , Feature Selection , Machine Learning Architectures , Greylag Goose Optimization , Multilayer Perceptron
[1] S. Bhandari, S. Pathak, and S. A. Jain. A literature review of early-stage diabetic retinopathy detection using deep learning and evolutionary computing techniques. Archives of Computational Methods in Engineering, 30(2):799–810, 2023.
[2] M. Gollapalli, A. Alansari, H. Alkhorasani, M. Alsubaii, R. Sakloua, R. Alzahrani, M. Al-Hariri, M. Alfares, D. AlKhafaji, R. Al Argan, and W. Albaker. A novel stacking ensemble for detecting three types of diabetes mellitus using a saudi arabian dataset: Pre-diabetes, t1dm, and t2dm. Computers in Biology and Medicine, 147:105757, 2022.
[3] M. Allam and M. Nandhini. Optimal feature selection using binary teaching learning based optimization algorithm. Journal of King Saud University - Computer and Information Sciences, 34(2):329–341, 2022.
[4] F. Barbetti, N. Rapini, R. Schiaffini, C. Bizzarri, and S. Cianfarani. The application of precision medicine in monogenic diabetes. Expert Review of Endocrinology Metabolism, 17(2):111–129, 2022.
[5] F. G. Preston, Y. Meng, J. Burgess, M. Ferdousi, S. Azmi, I. N. Petropoulos, S. Kaye, R. A. Malik, Y. Zheng, and U. Alam. Artificial intelligence utilising corneal confocal microscopy for the diagnosis of peripheral neuropathy in diabetes mellitus and prediabetes. Diabetologia, 65(3):457–466, 2022.
[6] B. Kurt, B. G¨urlek, S. Keskin, S. ¨Ozdemir, ¨O. Karadeniz, ˙I. B. Kırkbir, T. Kurt, S. ¨Unsal, C. Kart, N. Baki, and K. Turhan. Prediction of gestational diabetes using deep learning and bayesian optimization and traditional machine learning techniques. Medical & Biological Engineering & Computing, 61(7):1649– 1660, 2023.
[7] L. Ismail, H. Materwala, M. Tayefi, P. Ngo, and A. P. Karduck. Type 2 diabetes with artificial intelligence machine learning: Methods and evaluation. Archives of Computational Methods in Engineering, 29(1):313–333, 2022.
[8] H. A. Aliyu, I. O. Muritala, H. Bello-Salau, S. Mohammed, A. J. Onumanyi, and O.-O. Ajayi. Optimizing machine learning algorithms for diabetes data: A metaheuristic approach to balancing and tuning classifiers parameters. Franklin Open, 8:100153, 2024.
[9] U. Ahmed, G. F. Issa, M. A. Khan, S. Aftab, M. F. Khan, R. A. T. Said, T. M. Ghazal, and M. Ahmad. Prediction of diabetes empowered with fused machine learning. IEEE Access, 10:8529–8538, 2022.
[10] H. Shao, X. Liu, D. Zong, and Q. Song. Optimization of diabetes prediction methods based on combinatorial balancing algorithm. Nutrition & Diabetes, 14(1):1–13, 2024.
[11] C. Burchill L. C. A. Rosella, D. G. Manuel and T. A. Stukel. A population-based risk algorithm for the development of diabetes: development and validation of the diabetes population risk tool (dport). J. Epidemiol. Community Health, (7):613–620, 2011.
[12] J. A. L. Marques, F. N. B. Gois, J. P. do V. Madeiro, T. Li, and S. J. Fong. Artificial neural network-based approaches for computer-aided disease diagnosis and treatment. In A. K. Bhoi, V. H. C. de Albuquerque, P. N. Srinivasu, and G. Marques, editors, Cognitive and Soft Computing Techniques for the Analysis of Healthcare Data, pages 79–99. Academic Press, 2022.
[13] A. Dutta, M. K. Hasan, M. Ahmad, M. A. Awal, M. A. Islam, M. Masud, and H. Meshref. Early prediction of diabetes using an ensemble of machine learning models. International Journal of Environmental Research and Public Health, 19(19):Article 19, 2022.
[14] Bilal, A., Sun, G., Mazhar, S., Imran, A., & Latif, J. (2022). A Transfer Learning and U-Net-based automatic detection of diabetic retinopathy from fundus images. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 10(6), 663-674.
[15] C. C. Olisah, L. Smith, and M. Smith. Diabetes mellitus prediction and diagnosis from a data preprocessing and machine learning perspective. Computer Methods and Programs in Biomedicine, 220:106773, 2022.
[16] M. S. Ali, M. K. Islam, A. A. Das, D. U. S. Duranta, Mst. F. Haque, and M. H. Rahman. A novel approach for best parameters selection and feature engineering to analyze and detect diabetes: Machine learning insights. BioMed Research International, 2023(1):8583210, 2023.
[17] M. O’Neill T. Piggott J. D. Morgenstern, E. Buajitti and V. Goel. Predicting population health with machine learning: a scoping review. BMJ Open, 10(10):e037860, 2020.
[18] W. Xu, Z. Zhang, K. Hu, P. Fang, R. Li, D. Kong, M. Xuan, Y. Yue, D. She, and Y. Xue. Identifying metabolic syndrome easily and cost effectively using non-invasive methods with machine learning models. Diabetes, Metabolic Syndrome and Obesity, 16:2141–2151, 2023.
[19] S S S, J Surendiran, N Yuvaraj, M Ramkumar, CN Ravi, and RG Vidhya. Classification of diabetes using multilayer perceptron. In 2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE), pages 1–5, 2022.
[20] I. D. Dinov. Data science and predictive analytics: Biomedical and health applications using r. In 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), 2023.
[21] TM Le, TM Vo, TN Pham, and SVT Dao. A novel wrapper–based feature selection for early diabetes prediction enhanced with a metaheuristic. IEEE Access, 9:7869–7884, 2021.
[22] F Khademi, M Rabbani, H Motameni, and E Akbari. A weighted ensemble classifier based on woa for classification of diabetes. Neural Computing and Applications, 34(2):1613–1621, 2022.
[23] C Mallika and S Selvamuthukumaran. A hybrid crow search and grey wolf optimization technique for enhanced medical data classification in diabetes diagnosis system. International Journal of Computational Intelligence Systems, 14(1):157, 2021.
[24] R. Ahuja, P. Dixit, A. Banga, and S. C. Sharma. Classification algorithms for predicting diabetes mellitus: A comparative analysis. In M. S. Husain, M. H. B. M. Adnan, M. Z. Khan, S. Shukla, and F. U. Khan, editors, Pervasive Healthcare: A Compendium of Critical Factors for Success, pages 233–253. Springer International Publishing, 2022.
[25] AA Abdelhamid, SK Towfek, N Khodadadi, AA Alhussan, DS Khafaga, MM Eid, and A Ibrahim. Waterwheel plant algorithm: A novel metaheuristic optimization method. Processes, 11(5):Article 5, 2023.
[26] G. Rajarajeshwari and G. C. Selvi. Application of artificial intelligence for classification, segmentation, early detection, early diagnosis, and grading of diabetic retinopathy from fundus retinal images: A comprehensive review. IEEE Access, 12:172499–172536, 2024.
[27] Shi, Y., Fang, J., Li, J., Yu, K., Zhu, J., & Lu, Y. (2024). Fracture risk prediction in diabetes patients based on Lasso feature selection and Machine Learning. Computer Methods in Biomechanics and Biomedical
Engineering, 1-17.
[28] AH Alharbi, SK Towfek, AA Abdelhamid, A Ibrahim, MM Eid, DS Khafaga, N Khodadadi, L Abualigah, and M Saber. Diagnosis of monkeypox disease using transfer learning and binary advanced dipper throated optimization algorithm. Biomimetics, 8(3):Article 3, 2023.
[29] OY Dweekat and SS Lam. Optimized design of hybrid genetic algorithm with multilayer perceptron to predict patients with diabetes. Soft Computing, 27(10):6205–6222, 2023.
[30] Diabetes dataset. [dataset]. Retrieved March 13, 2024, from https://www.kaggle.com/datasets/mathchi/diabetes-data-set.