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

سال انتشار: 1404
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 9

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شناسه ملی سند علمی:

JR_IJDO-17-3_005

تاریخ نمایه سازی: 14 مرداد 1404

چکیده 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 ۹۷%, 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.

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نویسندگان

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.

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