Development of an explainable machine learning framework for early diabetes detection, multiclass classification, and risk stratification across age groups.
- 1School of Mathematical and Natural Sciences, Arizona State University, Phoenix, AZ, United States.
Abstract
To develop and evaluate a novel machine learning (ML) framework tailored to a clinical diabetes dataset and to assess whether demographic stratification enhances model performance and interpretability for multiclass diabetes classification. A clinical dataset of 264 patients' records was used to classify individuals including non-diabetic (36.4%), prediabetic (15.2%), and diabetic (48.5%) individuals. Several supervised learning models were trained using 80:20 train-test split and optimized using RandomizedSearchCV and 10-fold cross validation. Model performance was evaluated using the metrics accuracy, precision, recall and the F1-score. Area under the receiver operating characteristic curve (AUC) was calculated for the best generalizing model. A structured ML framework was developed for this dataset, incorporating preprocessing, model optimization, age stratification analysis age (<35 vs ≥35 years) and gender. SHAP was developed for model interpretability. Ensemble methods demonstrated superior performance in comparison to linear or single-tree approaches, with Gradient Boosting showing the most stable generalization with a test accuracy of 0.981 and stable cross validation accuracy of 0.972. AUC-ROC analysis using Gradient Boosting yielded good discriminative ability across the three diabetes classes: 0.991 (non-diabetic), 0.986 (prediabetic) and 0.972 (diabetic). Stratified analysis showed improved reliability in individuals aged ≥35 years (accuracy = 0.94, F1-score = 0.92), while performance in younger individuals was unstable due to small sample size. SHAP analysis identified HbA1c, BMI, and age as dominant predictors. This study presents a ML framework integrating age stratified modelling with explainable ML frameworks to improve interpretability. The findings offer clinically relevant results that can support clinical decision-making systems, individualized risk assessment, and potential applications for targeted intervention in diabetes progression.
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BibTeX
@article{lukhele2026development,
title = {{Development of an explainable machine learning framework for early diabetes detection, multiclass classification, and risk stratification across age groups.}},
author = {Nosizo Lukhele and Fahad Mostafa},
journal = {Frontiers in digital health},
year = {2026},
month = oct,
doi = {10.3389/fdgth.2026.1952669},
url = {https://europepmc.org/article/MED/42953022},
note = {CC BY},
}
RIS
TY - JOUR
TI - Development of an explainable machine learning framework for early diabetes detection, multiclass classification, and risk stratification across age groups.
AU - Nosizo Lukhele
AU - Fahad Mostafa
PY - 2026
DA - 2026/10/14
JO - Frontiers in digital health
DO - 10.3389/fdgth.2026.1952669
UR - https://europepmc.org/article/MED/42953022
AB - To develop and evaluate a novel machine learning (ML) framework tailored to a clinical diabetes dataset and to assess whether demographic stratification enhances model performance and interpretability for multiclass diabetes classification. A clinical dataset of 264 patients' records was used to classify individuals including non-diabetic (36.4%), prediabetic (15.2%), and diabetic (48.5%) individuals. Several supervised learning models were trained using 80:20 train-test split and optimized using RandomizedSearchCV and 10-fold cross validation. Model performance was evaluated using the metrics accuracy, precision, recall and the F1-score. Area under the receiver operating characteristic curve (AUC) was calculated for the best generalizing model. A structured ML framework was developed for this dataset, incorporating preprocessing, model optimization, age stratification analysis age (<35 vs ≥35 years) and gender. SHAP was developed for model interpretability. Ensemble methods demonstrated superior performance in comparison to linear or single-tree approaches, with Gradient Boosting showing the most stable generalization with a test accuracy of 0.981 and stable cross validation accuracy of 0.972. AUC-ROC analysis using Gradient Boosting yielded good discriminative ability across the three diabetes classes: 0.991 (non-diabetic), 0.986 (prediabetic) and 0.972 (diabetic). Stratified analysis showed improved reliability in individuals aged ≥35 years (accuracy = 0.94, F1-score = 0.92), while performance in younger individuals was unstable due to small sample size. SHAP analysis identified HbA1c, BMI, and age as dominant predictors. This study presents a ML framework integrating age stratified modelling with explainable ML frameworks to improve interpretability. The findings offer clinically relevant results that can support clinical decision-making systems, individualized risk assessment, and potential applications for targeted intervention in diabetes progression.
SN - 2673-253X
ER -