Prediction of 3-mouth glycemic control in type 2 diabetes mellitus based on machine learning algorithm

QIN Wei; GAO Min; SHEN Ying; SHI Yu-hui; WU Tao; ZHAO Ai; SUN Xin-ying

doi:10.16462/j.cnki.zhjbkz.2019.11.003

Volume 23 Issue 11

Nov. 2019

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QIN Wei, GAO Min, SHEN Ying, SHI Yu-hui, WU Tao, ZHAO Ai, SUN Xin-ying. Prediction of 3-mouth glycemic control in type 2 diabetes mellitus based on machine learning algorithm[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2019, 23(11): 1313-1317. doi: 10.16462/j.cnki.zhjbkz.2019.11.003

Citation:

QIN Wei, GAO Min, SHEN Ying, SHI Yu-hui, WU Tao, ZHAO Ai, SUN Xin-ying. Prediction of 3-mouth glycemic control in type 2 diabetes mellitus based on machine learning algorithm[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2019, 23(11): 1313-1317. doi: 10.16462/j.cnki.zhjbkz.2019.11.003

Citation:

QIN Wei, GAO Min, SHEN Ying, SHI Yu-hui, WU Tao, ZHAO Ai, SUN Xin-ying. Prediction of 3-mouth glycemic control in type 2 diabetes mellitus based on machine learning algorithm[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2019, 23(11): 1313-1317. doi: 10.16462/j.cnki.zhjbkz.2019.11.003

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Prediction of 3-mouth glycemic control in type 2 diabetes mellitus based on machine learning algorithm

doi: 10.16462/j.cnki.zhjbkz.2019.11.003

1.
Department of Social Medicine and Health Education, School of Public Health, Peking University, Beijing 100191, China
2.
Department of Epidemiology and Health Statistics, School of Public Health, Peking University, Beijing 100191, China

Funds:

National Natural Science Foundation of China 71673009

More Information

Corresponding author: SUN Xin-ying, E-mail: xysun@bjmu.edu.cn
Received Date: 2019-07-17
Rev Recd Date: 2019-08-08
Publish Date: 2019-11-10

Abstract

Abstract

Objective To evaluate the efficiency of Logistic regression algorithm and random forest algorithm in prediction of blood glucose control in patients with type 2 diabetes mellitus (T2DM) after 3 months, and explore the influencing factors of blood glucose control. Methods The data was extracted from baseline survey and follow-up information of patients with T2DM in Shunyi and Tongzhou Districts. The patient's 3-month glycosylated hemoglobin which was more than 6.5% was chosen as the outcome categorical variable. The random forest algorithm and Logistic algorithm were used to establish the prediction model. The predictive efficiency was evaluated with the area under receive operating characteristic curve (AUC) and accuracy rate. Results Factors affecting the patient's glycemic control included baseline fasting plasma glucose(P < 0.001), duration of disease(P < 0.001), smoking(P=0.026), static activity time(P=0.006), body mass index(overweight P=0.002, obesity P=0.011), bracelet use(P=0.028), and diabetes diet(P=0.002).The Logistic regression prediction model had an AUC of 0.738, a sensitivity of 72.9%, a specificity of 68.1%, and an accuracy of 71.2%. The random forest model had an AUC of 0.756, a sensitivity of 74.5%, a specificity of 69.5%, and an accuracy of 72.8%. Conclusions The efficiency of random forest is better than Logistic regression model, which can be applied to the prediction of blood glucose control and assist the management of diabetic patients.
- Diabetes mellitus type 2,
- Classification prediction,
- Random forest algorithm,
- Logistic regression algorithm

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References(17)

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