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Iranian Journal of Diabetes and Obesity، جلد ۱۷، شماره ۳، صفحات ۱۸۲-۱۹۲
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عنوان انگلیسی Predicting Diabetes Risk Using Machine Learning: A Comparative Study on the Yazd Health Study (YaHS)
چکیده انگلیسی مقاله 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.
 
کلیدواژه‌های انگلیسی مقاله Machine learning, Diabetes, Random forest
نویسندگان مقاله | Fateme Sefid
Department of Molecular Medicine,School of Advanced Technologies in Medicine,Shahid Sadoughi University of Medical Sciences Yazd Iran.


| Nazanin Norouzi-Ghahjavarestani
Department of Computer Science, Yazd University, Yazd, Iran.


| Malihe Soleymani-Tabasi
Department of Computer Science, Yazd University, Yazd, Iran.


| Jamal Zarepour-Ahmadabadi
Department of Computer Science, Yazd University, Yazd, Iran.


| Ghasem Azamirad
Department of Mechanical Engineering, Yazd University, Yazd, Iran.


| Mohamah yahya Vahidi Mehrjardi
Diabetes Research Center, Non-communicable Diseases Research Institute, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.


| Masoud Mirzaei
Yazd Cardiovascular Research Centre, Non-Communicable Diseases Research Centre, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.


| Seyed Mehdi Kalantar
Abortion Research Centre, Yazd Reproductive Sciences Institute, Shahid Sadoughi University of Medical Sciences, Yazd, Iran. Meybod Genetic Research Center, Yazd, Iran.
نشانی اینترنتی http://ijdo.ssu.ac.ir/browse.php?a_code=A-10-30-454&slc_lang=en&sid=1
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