RG Hyperparameter Optimization Approach for Improved Indirect Prediction of Blood Glucose Levels by Boosting Ensemble Learning

This paper proposes an RG hyperparameter optimization approach, based on a sequential use of random search (R) and grid search (G), for improving the blood glucose level prediction of boosting ensemble learning models. An indirect prediction of blood glucose levels in patients is performed, based on...

Full description

Saved in:
Bibliographic Details
Published in:Electronics (Basel) 2021-08, Vol.10 (15), p.1797
Main Authors: Wang, Yufei, Zhang, Haiyang, An, Yongli, Ji, Zhanlin, Ganchev, Ivan
Format: Article
Language:English
Subjects:
Blood
Cardiovascular disease
Cholesterol
Diabetes
Ensemble learning
Enzymes
Glucose
Human body
Insulin
Lipoproteins
Liver
Machine learning
Optimization
Risk factors
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper proposes an RG hyperparameter optimization approach, based on a sequential use of random search (R) and grid search (G), for improving the blood glucose level prediction of boosting ensemble learning models. An indirect prediction of blood glucose levels in patients is performed, based on historical medical data collected by means of physical examination methods, using 40 human body’s health indicators. The conducted experiments with real clinical data proved that the proposed RG double optimization approach helps improve the prediction performance of four state-of-the-art boosting ensemble learning models enriched by it, achieving 1.47% to 24.40% MSE improvement and 0.75% to 11.54% RMSE improvement.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics10151797