Application of Improved LightGBM Model in Blood Glucose Prediction

Wang, Yan; Wang, Tao

doi:10.3390/app10093227

Cited by 129Artificial Intelligence in HealthcareMachine Learning and Data ClassificationData Mining and Machine Learning Applications

Application of Improved LightGBM Model in Blood Glucose Prediction

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Wang, Yan, Wang, Tao·2020·Applied Sciences

DOI 10.3390/app10093227

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Abstract

In recent years, with increasing social pressure and irregular schedules, many people have developed unhealthy eating habits, which has resulted in an increasing number of patients with diabetes, a disease that cannot be cured under the current medical conditions, and can only be mitigated by early detection and prevention. A lot of human and material resources are required for the detection of the blood glucose of a large number of people in medical examination, while the integrated learning model based on machine learning can quickly predict the blood glucose level and assist doctors in treatment. Therefore, an improved LightGBM model based on the Bayesian hyper-parameter optimization algorithm is proposed for the prediction of blood glucose, namely HY_LightGBM, which optimizes parameters using a Bayesian hyper-parameter optimization algorithm based on LightGBM. The Bayesian hyper-parameter optimization algorithm is a model-based method for finding the minimum value of the function so as to obtain the optimal parameters of the LightGBM model. Experiments have demonstrated that the parameters obtained by the Bayesian hyper-parameter optimization algorithm are superior to those obtained by a genetic algorithm and random search. The improved LightGBM model based on the Bayesian hyper-parameter optimization algorithm achieves a mean square error of 0.5961 in blood glucose prediction, with a higher accuracy than the XGBoost model and CatBoost model.

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LGB uses a histogram-based decision tree approach, discretizing continuous features during the data-splitting process, and thereby allowing for effective management of sparse data.
“Additionally, LGB uses a histogram-based decision tree approach, discretizing continuous features during the data-splitting process, and thereby allowing for effective management of sparse data.”
From Machine learning-driven prediction model for successful weaning of patients from mechanical ventilation in ICU (2026) · cites this paper as [36]
Section 2.1, “LightGBM Model” (page not provided), describes LightGBM’s histogram-based decision-tree strategy and says its histogram algorithm discretizes continuous features to find a split point. It also describes feature bundling, but does not say that the histogram-based discretization thereby enables effective management of sparse data. (LLM, conf=0.84)
Experimental — accuracy not yet measured
LGB is particularly well equipped to address the challenges posed by imbalanced datasets.
“Furthermore, this model is particularly well equipped to address the challenges posed by imbalanced datasets.”
From Machine learning-driven prediction model for successful weaning of patients from mechanical ventilation in ICU (2026) · cites this paper as [36]
The Data Preprocessing section describes missing values and the training–test split, while the Introduction compares LightGBM with XGBoost on memory use and speed. Neither passage addresses performance on imbalanced datasets; searches for class imbalance, uneven sample distributions, and LightGBM sampling found no supporting passage in our copy. (LLM, conf=0.72)
Experimental — accuracy not yet measured
One key advantage of LGB is its ability to handle mixed-type features, which helps preserve the true distribution of these variables within the model.
“One key advantage of LGB is its ability to handle mixed-type features, this helps preserve the true distribution of these variables within the model.”
From Machine learning-driven prediction model for successful weaning of patients from mechanical ventilation in ICU (2026) · cites this paper as [36]
The Data Preprocessing section describes the dataset and how missing values were handled, while the LightGBM comparison passage reports model parameters and performance. Searches for mixed-type or categorical features and preservation of their distribution found no passage in our copy addressing the claim. (LLM, conf=0.70)
Experimental — accuracy not yet measured

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Authors: Wang, Y., & Wang, T.
Affiliations: Lanzhou University of Technology (CN)
Corresponding author: Wang, Tao
ORCID: Wang, Yan
Wang, Tao
Venue: Applied Sciences
Publisher: MDPI AG
Published: 2020-05-06
Volume: 10
Issue: 9
Pages: 3227
ISSN: 2076-3417 (ISSN-L)
DOI: 10.3390/app10093227
OpenAlex: W3021916839
Cited by: 129
References: 28
Citations by year: 2026: 21 · 2025: 34 · 2024: 32 · 2023: 15 · 2022: 14 · 2021: 13
FWCI: 8.92
Type: journal article
Language: en
Open access: gold
dx.doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Applied Sciences (publishedVersion, cc-by)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: Key R &amp; D plan of Gansu Province (18YF1GA060)
Topics: Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
Keywords: blood glucose prediction, LightGBM, Bayesian hyperparameter optimization, diabetes mellitus, hyperparameter optimization, genetic algorithm comparison, random search comparison, XGBoost comparison

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Application of Improved LightGBM Model in Blood Glucose Prediction

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