Predicting Diabetes Risk Using Machine Learning: A Comparative Study on the Yazd Health Study (YaHS)

Sefid, Fateme; Norouzi-Ghahjavarestani, Nazanin; Soleymani-Tabasi, Malihe; Zarepour-Ahmadabadi, Jamal; Azamirad, Ghasem; Vahidi Mehrjardi, Mohamah yahya; Mirzaei, Masoud; Kalantar, Seyed Mehdi

doi:10.18502/ijdo.v17i3.19267

Volume 17, Issue 3 (7-2025) IJDO 2025, 17(3): 182-192 | Back to browse issues page

‎ 10.18502/ijdo.v17i3.19267

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Sefid F, Norouzi-Ghahjavarestani N, Soleymani-Tabasi M, Zarepour-Ahmadabadi J, Azamirad G, Vahidi Mehrjardi M Y, et al . Predicting Diabetes Risk Using Machine Learning: A Comparative Study on the Yazd Health Study (YaHS). IJDO 2025; 17 (3) :182-192
URL: http://ijdo.ssu.ac.ir/article-1-967-en.html

Predicting Diabetes Risk Using Machine Learning: A Comparative Study on the Yazd Health Study (YaHS)

Fateme Sefid

, Nazanin Norouzi-Ghahjavarestani

, Malihe Soleymani-Tabasi

, Jamal Zarepour-Ahmadabadi

, Ghasem Azamirad

, Mohamah yahya Vahidi Mehrjardi

, Masoud Mirzaei

, Seyed Mehdi Kalantar ^*

Abortion Research Centre, Yazd Reproductive Sciences Institute, Shahid Sadoughi University of Medical Sciences, Yazd, Iran. Meybod Genetic Research Center, Yazd, Iran.

Abstract: (22 Views)

Diabetes is a chronic disease that can significantly affect health at the global level, highlighting the importance of accurate early risk prediction to support prevention and management efforts. This study aims to evaluate the effectiveness of some efficient machine learning algorithms: Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Naïve Bayes (NB), and Decision Tree (DT) in diabetes risk prediction using dataset acquired from Yazd Health Study (YaHS). Extensive preprocessing steps, including data cleaning, class imbalance handling through Synthetic Minority Oversampling Technique and Edited Nearest Neighbors (SMOTEENN), and feature selection, are applied to enhance the performance of models. Among the evaluated machine learning algorithms, the Random Forest classifier achieved the highest performance with an accuracy of 97%, outperforming other methods in terms of predictive capability. The findings highlight the vital importance of effective data preprocessing and algorithm selection in developing reliable predictive models from healthcare datasets.

Keywords: Machine learning, Diabetes, Random forest

Full-Text [PDF 805 kb] (8 Downloads)

Type of Study: Research | Subject: Special
Received: 2025/05/29 | Accepted: 2025/06/30 | Published: 2025/07/28

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