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All Journal International Journal of Advances in Applied Sciences
Mohammad Saeed Al Ghamdi
King Abdulaziz University
Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Using machine learning to understand the root causes of type 2 diabetes in Saudi Arabia Mohammad Saeed Al Ghamdi; Alaa Omar Khadidos; Adel Omar Khadidos
International Journal of Advances in Applied Sciences Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i2.pp790-803

Abstract

This study examines the elevated prevalence of type 2 diabetes mellitus (T2DM) in Saudi Arabia by integrating a large-scale, regionally specific dataset from the Saudi Ministry of Health (100,000 patient records). The primary contribution is this Saudi-focused data integration—addressing a critical gap in prior studies that rely on global datasets (e.g., PIMA, UK Biobank)—and a comparative evaluation of traditional machine learning (ML) models (random forest (RF), adaptive boosting (AdaBoost)) and few shot large language models (LLMs) for early risk detection. Key features include age, body mass index (BMI), HbA1c, blood glucose, hypertension, and smoking history. Class imbalance (9% diabetic) was resolved using synthetic minority over-sampling technique with edited nearest neighbors (SMOTEENN). Ensemble ML models achieved up to 97% accuracy, while few-shot LLM reached 98% across accuracy, precision, recall, and F1-score (area under the curve (AUC) 0.99). Feature importance confirmed that HbA1c and glucose were the top predictors. This regionally tailored, high performing framework enables early detection of T2DM and culturally sensitive interventions in high-prevalence settings such as Saudi Arabia.
Co-Authors Adel Omar Khadidos Alaa Omar Khadidos