Article

Multi-Dataset Diabetes Prediction Using Stacking Ensemble Learning and Explainable Artificial Intelligence

2026-06-12

Abstract excerpt

<title>Abstract</title> <p>Diabetes mellitus affects more than 530 million people worldwide and leads to severe complications if undetected. Most existing machine learning studies rely on a single dataset, constraining the generalisability of their findings. A five-model stacking ensemble—Random Forest, XGBoost, LightGBM, Support Vector Classifier, and Deep Neural Network with Logistic Regression as meta-classifi...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: ecf451c9-7c14-5cc7-9589-a2d6da92fd37
DOI: 10.21203/rs.3.rs-9884215/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Multi-Dataset Diabetes Prediction Using Stacking Ensemble Learning and Explainable Artificial IntelligenceDOI 10.21203/rs.3.rs-9884215/v1Read

Development of Explainable Machine Learning Framework for Early Detection and Risk Stratification of Diabetes in Age Specific VariationsSemantic proximity Read

Internal and External Validation of Machine Learning Algorithms Versus FINDRISC for Incident Type 2 Diabetes: A Transparent, Explainable Benchmark Using SHAPSemantic proximity Read

An Explainable Stacking Ensemble with Cost-Sensitive Threshold Optimization for Gestational Diabetes Risk PredictionSemantic proximity Read

DiaHealth: Early Prediction of Type-2 Diabetes with Associated Risk Factors Using Machine Learning and Explainable AISemantic proximity Read

Diabetes Prediction Using Machine LearningSemantic proximity Read

An Explainable Maximum Expected Utility-Optimized Ensemble for Early Prediction of Gestational Diabetes MellitusSemantic proximity Read

An Interpretable Machine Learning Model for the Diagnosis of Coronary Heart Disease in Patients with Type 2 Diabetes Mellitus: A Multi-Center Cohort StudySemantic proximity Read

Comparative Performance of Linear Regression and Machine Learning Models for Predicting Glycemic Status in Uncontrolled Type 2 Diabetes: SHAP-Based AnalysisSemantic proximity Read

Towards Reliable Diabetes Prediction: Innovations in Data Engineering and Machine Learning ApplicationsSemantic proximity Read

Development and validation of a type 2 diabetes machine learning classification model for EHR-based diagnostics and clinical decision supportSemantic proximity Read

Long-term Prediction of Severe Hypoglycemia in Type 2 Diabetes Based on Multi-view Co-trainingSemantic proximity Read

An Integrative Approach for Identification of Pre-Diabetes Clusters and Glycaemic Prediction using Ensemble Machine LearningSemantic proximity Read