Performance Benchmarking of Optimized Stacked Ensemble Frameworks in Blood Glucose Level ( BGL ) Prediction
Year: 2026
Type: article
Abstract: ABSTRACT This paper presents a study of different stacked ensemble techniques for long‐term Blood Glucose Level (BGL) prediction in adults. Stacked ensemble models were developed by combining various machine learning techniques, namely eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Random Forest (RF). These machine learning models served as base learners for a Linear Regression (LR) based meta‐learner model. The models were evaluated on 30% held‐out test samples using a suite of different evaluation metrics, incorporating 10‐Fold Time Series Cross‐Validation (TSCV). The XGBoost technique was effectively used for feature selection. The hyperparameters were optimized using the Grid Search (GS) and Bayesian Optimization (BO) techniques. The findings highlight a superior performance of the ensemble model optimized using a BO approach. The results confirmed the superior performance of the ensemble model designed using XGBoost and AdaBoost techniques as base learners for the LR model, incorporating the BO approach for hyperparameters optimization.
Source: Engineering Reports
DOI: 10.1002/eng2.70787
Authors: Ashwani Kharola, Harvinder Singh, Jeewan Singh, Purnendu Shekar Pandey, Nitin Kumar, Ajay Kumar, Abhijit Bhowmik, Vivek John, Yohannes Mengist
Institutions: Graphic Era University, Jadara University, Chandigarh University, G.L. Bajaj Institute of Technology and Management Greater Noida, Chitkara University, Noida Institute of Engineering and Technology, Lovely Professional University, Saveetha University, Uttaranchal University, Debre Markos University
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,270
OpenAlex ID: W7156791958