Abstract: Diabetes is one of the most prevalent chronic diseases globally, with significant mortality and morbidity rates. Early and accurate diagnosis plays a critical role in managing and mitigating its impact. However, achieving high diagnostic accuracy while ensuring interpretability remains a key challenge in medical machine learning applications. This paper proposes an interpretable and accurate hybrid framework for diabetes prediction that integrates Support Vector Machine Rule Extraction (SVMRE), Fuzzy Analytic Hierarchy Process (Fuzzy AHP), and Sugeno fuzzy inference. The primary objective of this study is to enhance prediction accuracy while enabling the extraction of meaningful and explainable decision rules derived from SVM models. To address the black-box nature of traditional SVM models, fuzzy rules are extracted and embedded into a Sugeno fuzzy inference system. Attribute importance is quantified through Fuzzy AHP based on expert consultation, ensuring medically relevant decision-making. Furthermore, to overcome rule redundancy and complexity, the coefficient of variation is computed for each rule and optimized using a Nearest Neighbor (NN) approach, which clusters rules with adjacent variation values. The proposed framework is evaluated using a real-world diabetes dataset from Sylhet, Bangladesh. It achieves a prediction accuracy of 84.62 per cent, outperforming several conventional methods. Compared to other competitive approaches found in recent literature, such as fuzzy grey wolf optimization and neuro-fuzzy systems, our method demonstrates superior balance between interpretability, computational efficiency, and classification performance. This study confirms that integrating rule-based learning, fuzzy expert systems, and statistical optimization provides a robust and interpretable approach for diabetes prediction. The framework aligns with Sustainable Development Goal 3 (SDG 3) by promoting early detection and decision support for non-communicable diseases in healthcare systems.

Muhammadun , Baity Jannaty, Rajermani Thinakaran and Taufik Rachman, “Support Vector Machine with Rule Extraction to Improve Diabetes Prediction Using Fuzzy AHP-Sugeno and Nearest Neighbor” International Journal of Advanced Computer Science and Applications(IJACSA), 16(5), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160572

@article{2025,
title = {Support Vector Machine with Rule Extraction to Improve Diabetes Prediction Using Fuzzy AHP-Sugeno and Nearest Neighbor},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160572},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160572},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {5},
author = {Muhammadun and Baity Jannaty and Rajermani Thinakaran and Taufik Rachman}
}