Application of Improved LightGBM Model in Blood Glucose Prediction
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Artificial Intelligence in Healthcare · Spectroscopy Techniques in Biomedical and Chemical Research · Spectroscopy and Chemometric Analyses
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China
Funding
Key R & D plan of Gansu Province
References (30)
Show source-provided references
1. American Diabetes Association (2019). Standards of Medical Care in Diabetes—2019. Diabetes Care, 42, S1–S2.10.2337/dc19-Sint01 ↗
2. Definition, Classification and Diagnosis of Diabetes Mellitus10.1055/s-0034-1366278 ↗
3. IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 204510.1016/j.diabres.2018.02.023 ↗
4. WHO.int (2020, March 26). Diabetes. Available online: https://www.who.int/news-room/fact-sheets/detail/diabetes.
5. Global healthcare expenditure on diabetes for 2010 and 203010.1016/j.diabres.2010.01.026 ↗
6. Diabetes mellitus complications and therapeutics
7. He, J. (2019, January 27–30). Blood Glucose Concentration Prediction Based on Canonical Correlation Analysis. Proceedings of the 38th China Control Conference, Guangzhou, China.10.23919/ChiCC.2019.8865767 ↗
8. Copy number variation in plasma as a tool for lung cancer prediction using Extreme Gradient Boosting (XGBoost) classifier10.1111/1759-7714.13204 ↗
9. Features Importance in Classification Models for Colorectal Cancer Cases Phenotype in Indonesia10.1016/j.procs.2019.08.172 ↗
10. Chang, W., Liu, Y., Xiao, Y., Yuan, X., Xu, X., Zhang, S., and Zhou, S. (2019). A Machine-Learning-Based Prediction Method for Hypertension Outcomes Based on Medical Data. Diagnostics, 9.10.3390/diagnostics9040178 ↗
11. Azeez, O., and Wang, Q. (2019). XGBoost Model for Chronic Kidney Disease Diagnosis. IEEE/ACM Trans. Comput. Biol. Bioinform.
12. Chang, W., Liu, Y., Xiao, Y., Xu, X., Zhou, S., Lu, X., and Cheng, Y. (2019). Probability Analysis of Hypertension-Related Symptoms Based on XGBoost and Clustering Algorithm. Appl. Sci., 9.10.3390/app9061215 ↗
13. Application of cat boost model based on machine learning in prediction of severe HFMD
14. Chen, T., and Guestrin, C. (2016). Xgboost: A scalable tree boosting system. Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, ACM.10.1145/2939672.2939785 ↗
15. Rani, S., Suri, B., and Goyal, R. (2019). On the Effectiveness of Using Elitist Genetic Algorithm in Mutation Testing. Symmetry, 11.10.3390/sym11091145 ↗
16. A genetic algorithm for the characterization of hyperelastic materials
17. Random Search for Hyper-Parameter Optimization
18. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, The MIT Press.
19. Gao, X., Luo, H., Wang, Q., Zhao, F., Ye, L., and Zhang, Y. (2019). A Human Activity Recognition Algorithm Based on Stacking Denoising Autoencoder and LightGBM. Sensors, 19.10.3390/s19040947 ↗
20. Choi, S., and Hur, J. (2020). An Ensemble Learner-Based Bagging Model Using Past Output Data for Photovoltaic Forecasting. Energies, 13.10.3390/en13061438 ↗
21. Cordeiro, J.R., Postolache, O., and Ferreira, J.C. (2019). Child’s Target Height Prediction Evolution. Appl. Sci., 9.10.3390/app9245447 ↗
22. Study on a prediction of p2p network loan default based on the machine learning lightgbm and xgboost algorithms according to different high dimensional data cleaning10.1016/j.elerap.2018.08.002 ↗
23. LightGBM-PPI: Predicting protein-protein interactions through LightGBM with multi-information fusion10.1016/j.chemolab.2019.06.003 ↗
24. Formation lithology classification using scalable gradient boosted decision trees10.1016/j.compchemeng.2019.06.001 ↗
25. Constrained bayesian optimization with noisy experiments10.1214/18-BA1110 ↗
26. GA-SVM based feature selection and parameter optimization in hospitalization expense modeling
27. Prediction models of human plasma protein binding rate and oral bioavailability derived by using GA–CG–SVM method10.1016/j.jpba.2008.03.023 ↗
28. An efficient intrusion detection system based on hypergraph—Genetic algorithm for parameter optimization and feature selection in support vector machine10.1016/j.knosys.2017.07.005 ↗
29. Genetic algorithm based feature selection and parameter optimization for support vector regression applied to semantic textual similarity10.1007/s12204-015-1602-2 ↗
30. Putatunda, S., and Rama, K. (2018). A Comparative Analysis of Hyperopt as Against Other Approaches for Hyper-Parameter Optimization of XGBoost. Proceedings of the 2018 International Conference on Signal Processing and Machine Learning (SPML ’18), Association for Computing Machinery.10.1145/3297067.3297080 ↗