Early-Stage Diabetes Detection Using Age-Based Feature Selection and Machine Learning Methods

Betül Uzbaş

doi:10.54287/gujsa.1753574

Early-Stage Diabetes Detection Using Age-Based Feature Selection and Machine Learning Methods

Abstract

Diabetes is spreading rapidly around the world, and there is an urgent need for governments to develop comprehensive strategies for diabetes detection. Early detection of diabetes is important for early initiation of treatment. In this paper, Data Mining (DM) and Machine Learning (ML) techniques are used to detect early diabetes by age from survey data. The dataset was divided into 3 groups (young, middle-aged, elderly), and a unique feature selection process was performed by averaging the feature importance obtained in Random Forest (RF), Gradient Boosting (GB), and eXtreme Gradient Boosting (XGBoost) algorithms for each group, and the features that should be considered in diabetes detection according to age groups were determined. Then, the features selected for each age group were classified using different ML methods. Accuracies of 96.77%, 98.10% and 99% were obtained for the young, middle-aged and elderly groups, respectively. The characteristics that should be taken into account in the assessment of diabetes according to age groups were also identified.

Keywords

References

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Details

Primary Language

English

Subjects

Computing Applications in Health

Journal Section

Research Article

Authors

Betül Uzbaş ^*
0000-0002-0255-5988
Türkiye

Publication Date

December 31, 2025

Submission Date

July 29, 2025

Acceptance Date

October 21, 2025

Published in Issue

Year 2025 Volume: 12 Number: 4

DOI

https://doi.org/10.54287/gujsa.1753574

IZ

https://izlik.org/JA87CS77TK

Cite

RIS / Bibtex

APA

Uzbaş, B. (2025). Early-Stage Diabetes Detection Using Age-Based Feature Selection and Machine Learning Methods. Gazi University Journal of Science Part A: Engineering and Innovation, 12(4), 953-965. https://doi.org/10.54287/gujsa.1753574

AMA

1.Uzbaş B. Early-Stage Diabetes Detection Using Age-Based Feature Selection and Machine Learning Methods. GU J Sci, Part A. 2025;12(4):953-965. doi:10.54287/gujsa.1753574

Chicago

Uzbaş, Betül. 2025. “Early-Stage Diabetes Detection Using Age-Based Feature Selection and Machine Learning Methods”. Gazi University Journal of Science Part A: Engineering and Innovation 12 (4): 953-65. https://doi.org/10.54287/gujsa.1753574.

EndNote

Uzbaş B (December 1, 2025) Early-Stage Diabetes Detection Using Age-Based Feature Selection and Machine Learning Methods. Gazi University Journal of Science Part A: Engineering and Innovation 12 4 953–965.

IEEE

[1]B. Uzbaş, “Early-Stage Diabetes Detection Using Age-Based Feature Selection and Machine Learning Methods”, GU J Sci, Part A, vol. 12, no. 4, pp. 953–965, Dec. 2025, doi: 10.54287/gujsa.1753574.

ISNAD

Uzbaş, Betül. “Early-Stage Diabetes Detection Using Age-Based Feature Selection and Machine Learning Methods”. Gazi University Journal of Science Part A: Engineering and Innovation 12/4 (December 1, 2025): 953-965. https://doi.org/10.54287/gujsa.1753574.

JAMA

1.Uzbaş B. Early-Stage Diabetes Detection Using Age-Based Feature Selection and Machine Learning Methods. GU J Sci, Part A. 2025;12:953–965.

MLA

Uzbaş, Betül. “Early-Stage Diabetes Detection Using Age-Based Feature Selection and Machine Learning Methods”. Gazi University Journal of Science Part A: Engineering and Innovation, vol. 12, no. 4, Dec. 2025, pp. 953-65, doi:10.54287/gujsa.1753574.

Vancouver

1.Betül Uzbaş. Early-Stage Diabetes Detection Using Age-Based Feature Selection and Machine Learning Methods. GU J Sci, Part A. 2025 Dec. 1;12(4):953-65. doi:10.54287/gujsa.1753574