The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Application of Improved LightGBM Model in Blood Glucose Prediction
Open accessAbstract
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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“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]
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“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]
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“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]
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XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569
LightGBM: A Highly Efficient Gradient Boosting Decision Tree
(2017) · HAL · Cited by 10,512
Self-citation(2012) · Cited by 8,009
IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045
Cho, N. H., Shaw, J. E., Karuranga, S., Huang, Y., da Rocha Fernandes, J. D., Ohlrogge, A. W., & Malanda, B. (2018) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2018.02.023 · Cited by 7,283
Global healthcare expenditure on diabetes for 2010 and 2030
Zhang, P., Zhang, X., Brown, J., Vistisen, D., Sicree, R., Shaw, J., & Nichols, G. (2010) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2010.01.026 · Cited by 1,145
Definition, Classification and Diagnosis of Diabetes Mellitus
Kerner, W., & Brückel, J. (2014) · Experimental and Clinical Endocrinology & Diabetes · doi:10.1055/s-0034-1366278 · Cited by 897
XGBoost Model for Chronic Kidney Disease Diagnosis
Ogunleye, A., & Wang, Q. (2019) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2019.2911071 · Cited by 768
Ma, X., Sha, J., Wang, D., Yu, Y., Qian, Y., & Niu, X. (2018) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2018.08.002 · Cited by 445
Diabetes mellitus: complications and therapeutics.
Tripathi, B. K., & Srivastava, A. K. (2006) · PubMed · Cited by 390
Introduction: Standards of Medical Care in Diabetes—2019
Frías, J. P. (2018) · Diabetes Care · doi:10.2337/dc19-sint01 · Cited by 331
Research works printed but not linked: 1
- Wang, 2019, Application of cat boost model based on machine learning in prediction of severe HFMD, Chin. J. Infect. Control, 18, 18
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Metadata
- Authors
- Wang, Y., & Wang, T.
- Affiliations
- Lanzhou University of Technology (CN)
- Corresponding author
- Wang, Tao
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2020-05-06
- Volume
- 10
- Issue
- 9
- Pages
- 3227
- ISSN
- 2076-3417 (ISSN-L)
- 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
- Locations
- Applied Sciences (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Key R & 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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