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

Year 2025, Volume: 12 Issue: 4, 953 - 965, 31.12.2025

Betül Uzbaş

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

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

Data Preprocessing , Diabetes Prediction , Feature Selection , Machine Learning

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