Explainable diabetes prediction using a stacked ensemble framework
- 1Department of Computer Science, University of Management and Technology, Lahore (Sialkot), Pakistan
- 2School of Computer Sciences, Universiti Sains Malaysia, USM Penang, Malaysia
Abstract
Diabetes is a chronic disease that significantly increases the risk of serious complications such as cardiovascular disorders and kidney failure. Early detection through predictive modeling can lead to timely interventions and significantly improve patient health outcomes. Several machine learning approaches have been proposed for predicting diabetes, but the main focus has been on improving prediction accuracy, while interpretability has received limited attention. To address this gap, we present a robust and explainable machine learning framework based on a stacked ensemble model that uses Random Forest, Support Vector Machine, and Gradient Boosting as base learners and Catboost as the meta-learner. The model was trained on the PIMA Indians Diabetes dataset using a preprocessing pipeline that included standard scaling, analysis of variance (ANOVA)- F-score-based feature selection, and class balancing with the synthetic minority oversampling technique (SMOTE). The proposed ensemble model outperformed the latest methods with an accuracy of 86%. We integrated explainable AI techniques such as Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanation (SHAP) to enhance transparency, which provide both local and global interpretability by identifying the most influential features contributing to each prediction, thus supporting more informed and trustworthy decision-making in healthcare applications.
Related
- An AutoML-based stacked ensemble framework for early prediction of maternal health risks
- Hybrid Explainable Deep Ensemble Framework with self-attention mechanism for early prediction of depression severity using PHQ-9 clinical and behavioral data
- An explainable ensemble machine learning approach for coefficient of compressibility prediction of Chengdu clay
- Explainable AI framework for Alzheimer’s disease prediction using multimodal data
- A machine learning-driven and explainable prediction model for in-hospital mortality in patients with diabetes mellitus and congestive heart failure: integrating XGBoost and SHAP approaches.
BibTeX
@article{awan2026explainable,
title = {{Explainable diabetes prediction using a stacked ensemble framework}},
author = {Saher Fatima Awan and Kainat Irfan and Umair Muneer Butt and Sukumar Letchmunan and Fadratul Hafinaz Hassan},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0352313},
url = {https://doi.org/10.1371/journal.pone.0352313},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Explainable diabetes prediction using a stacked ensemble framework
AU - Saher Fatima Awan
AU - Kainat Irfan
AU - Umair Muneer Butt
AU - Sukumar Letchmunan
AU - Fadratul Hafinaz Hassan
PY - 2026
DA - 2026/09/15
JO - PLOS One
DO - 10.1371/journal.pone.0352313
UR - https://doi.org/10.1371/journal.pone.0352313
AB - Diabetes is a chronic disease that significantly increases the risk of serious complications such as cardiovascular disorders and kidney failure. Early detection through predictive modeling can lead to timely interventions and significantly improve patient health outcomes. Several machine learning approaches have been proposed for predicting diabetes, but the main focus has been on improving prediction accuracy, while interpretability has received limited attention. To address this gap, we present a robust and explainable machine learning framework based on a stacked ensemble model that uses Random Forest, Support Vector Machine, and Gradient Boosting as base learners and Catboost as the meta-learner. The model was trained on the PIMA Indians Diabetes dataset using a preprocessing pipeline that included standard scaling, analysis of variance (ANOVA)- F-score-based feature selection, and class balancing with the synthetic minority oversampling technique (SMOTE). The proposed ensemble model outperformed the latest methods with an accuracy of 86%. We integrated explainable AI techniques such as Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanation (SHAP) to enhance transparency, which provide both local and global interpretability by identifying the most influential features contributing to each prediction, thus supporting more informed and trustworthy decision-making in healthcare applications.
SN - 1932-6203
ER -