Journal of Isfahan Medical School

Journal of Isfahan Medical School

Diabetes prediction using the Adaboost algorithm in active men

Document Type : Original Article (s)

Authors
Hamid Agha-Alinejad 1
Leila Fasihi 2
Mohammad Hossein Ghahremani 3
Mozhan Agha-Alinejad 4
1 Professor of Exercise Physiology, Department of Physical Education & Sport Sciences, Faculty of Humanities, Tarbiat Modares University, Tehran, Iran
2 PhD of Exercise Physiology, Department of Physical Education & Sport Sciences, Faculty of Humanities, Tarbiat Modares University, Tehran, Iran
3 MSc. of Exercise Physiology, Department of Physical Education & Sport Sciences, Faculty of Humanities, Tarbiat Modares University, Tehran, Iran
4 MSc Student of Exercise Physiology, Department of Physical Education & Sport Sciences, Faculty of Physical Education & Sport Sciences, Tehran University, Tehran, Iran
10.48305/jims.v44.i851.0137
Abstract

Background: Diabetes affects cardiovascular diseases, vision loss, and kidney disease. Data mining techniques help doctors predict the disease early and treat it accurately. Given the role of physical activity in preventing diabetes, this study examined its effect alongside other risk factors. Therefore, the main objective of this study was to predict diabetes in active men using the Adaboost algorithm.
Methods: In this applied-developmental study, the records of 500 male patients aged 20-80 years with a history of regular physical activity (at least 3 90-minute sessions per week) who had visited Milad and Ayatollah Kashani hospitals in Tehran in the past 10 years were selected as samples. 20 anthropometric, genetic, family history, environmental and physiological variables were selected as input features of the algorithm. The criteria of sensitivity, accuracy and precision were used to evaluate the performance of the proposed algorithm. MATLAB (2024) software was used for data analysis.
Results: The results showed that the Adaboost algorithm was able to predict diabetes in active men with an accuracy of 73.1% and precision of 75.3%.
Conclusion: This model can be used as an auxiliary tool in the initial screening of diabetes in medical centers.

Highlights

Hamid Agha-Alinejad: Google Scholar

Leila Fasihi: Google Scholar

Keywords
Diabetes
Adaboost
Men
Active
Prediction
Subjects
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Journal of Isfahan Medical School
Volume 44, Issue 851
3rd Week, April
March and April 2026
Pages 137-142

  • Receive Date 09 March 2025
  • Accept Date 16 May 2026