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  4. Vol 7, Issue 8, 2023

Application of XGBoost Model in the Field of Diabetes Prediction

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DOI: 10.23977/acss.2023.070804 | Downloads: 13 | Views: 327

Author(s)

Yilin Wang 1, Wenhao Jiang 2

Affiliation(s)

1 School of Mechanical and Electrical Engineering, Wuhan University of Technology, Wuhan, 430070, China
2 College of Medical Information Engineering, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an, 271016, China

Corresponding Author

Yilin Wang

ABSTRACT

Diabetes is a metabolic disorder that threatens people's health, and standardized screening is an important way to diagnose and treat it early. It is low cost and high efficiency to screen through data, therefore, to predict diabetes early has become crucial. Diabetics were taken as the research subject in this paper, and XGBoost algorithm was used to process the patient's data from physical examination, so a model for predicting diabetes was established to predict the blood glucose level of patients and to explore the application of XGBoost model in the field of diabetes prediction. The experimental results have been shown that the mean square error of the sample using this model has been just 0.0598, and it have been verified that the prediction error of the model is small and the accuracy is high, which will soon provide a good means for the pre-screening and clinical prediction of diabetes.

KEYWORDS

XGBoost model; diabetes prediction; standardized screening

CITE THIS PAPER

Yilin Wang, Wenhao Jiang, Application of XGBoost Model in the Field of Diabetes Prediction. Advances in Computer, Signals and Systems (2023) Vol. 7: 29-36. DOI: http://dx.doi.org/10.23977/acss.2023.070804.

REFERENCES

[1] Wenlong Qu , Yiyi Li, Lei Z .Application of XGBoost algorithm in diabetic blood glucose prediction[J].Jilin Normal University Journal(Natural Science Edition), 2019. 
[2] Cahn A, Shoshan A, Sagiv T, et al. Prediction of progression from pre-diabetes to diabetes. development and validation of a machine learning model[J]. Diabetes/Metabolism Research and Reviews, 2020, 36(2):e3252. 
[3] Moreno LM. Vergara J, Alacon R. Predictive risk model for the diagnosis of diabetes mellitus type 2 in a follow-up study 15 years om. PROD12 Study [J]. European Journal of Pubic Heath, 2019, 29(1):178-182. 
[4] Zuo D, Zhao XL, Dai XL. Construction and verification of hypoglycemia risk prediction model in patients with type 2 diabetes [J]. Journal of nursing science, 2021, 36(1):30-33. 
[5] Su X, Guo CR, Li YJ. Research on precision milling quality prediction based on XGBoost algorithm [J]. Machine building and automation, 2023, 52(02):72-76. DOI:10. 19344/j. cnki. issn1671-5276. 2023. 02. 020.

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