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

نویسندگان
Norouzi-Ghahjavarestani, Nazanin ID 1
Soleymani-Tabasi, Malihe ID 2
Zarepour-Ahmadabadi, Jamal ID 3
Mirzaei, Masoud ID 4
Sefid, Fateme ID 5
Azamirad, Ghasem ID 6
Vahidi Mehrjardi, Mohamah yahya ID 7
Kalantar, Seyed Mehdi ID 8

1 Department of Computer Science, Yazd University, Yazd, Iran.

2 Department of Computer Science, Yazd University, Yazd, Iran.

3 Department of Computer Science, Yazd University, Yazd, Iran.

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

5 Department of Molecular Medicine,School of Advanced Technologies in Medicine,Shahid Sadoughi University of Medical Sciences Yazd Iran.

6 Department of Mechanical Engineering, Yazd University, Yazd, Iran.

7 Diabetes Research Center, Non-communicable Diseases Research Institute, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.

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

doi
10.18502/ijdo.v17i3.19267
چکیده

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), ...

کلیدواژه‌ها
Machine learning, Diabetes, Random forest