Intelligent classification of hypoglycemia treatment plan for patients with type 2 diabetes based on ensemble learning

SONG Ya'nan; JIN Xinye; ZHANG Ying; CHEN Kang; YING Jun; XUE Wanguo; MU Yiming

doi:10.3969/j.issn.2095-5227.2019.08.004

Volume 40 Issue 8

Aug. 2019

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ACADEMIC JOURNAL OF CHINESE PLA MEDICAL SCHOOL > 2019 > 40(8): 719-724. > DOI: 10.3969/j.issn.2095-5227.2019.08.004

SONG Ya'nan, JIN Xinye, ZHANG Ying, CHEN Kang, YING Jun, XUE Wanguo, MU Yiming. Intelligent classification of hypoglycemia treatment plan for patients with type 2 diabetes based on ensemble learning[J]. ACADEMIC JOURNAL OF CHINESE PLA MEDICAL SCHOOL, 2019, 40(8): 719-724. DOI: 10.3969/j.issn.2095-5227.2019.08.004

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Intelligent classification of hypoglycemia treatment plan for patients with type 2 diabetes based on ensemble learning

1.
Medical Big Data Center, Chinese PLA General Hospital, Beijing 100853, China
2.
Department of Endocrinology, the First Medical Center, Chinese PLA General Hospital, Beijing 100853, China
3.
Department of Ophthalmology, the First Medical Center, Chinese PLA General Hospital, Beijing 100853, China

Funds: Supported by Beijing Municipal Science and Technology Commission (D141107005314004)

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Corresponding author:
MU Yiming. Email: muyiming@301hospital.com.cn
Received Date: April 07, 2019
Available Online: July 06, 2021

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Abstract

Abstract

Objective To apply Adaboost in the determination of hypoglycemia treatment plan in patients with type 2 diabetes.
Methods Clinical data about 3 005 patients with type 2 diabetes hospitalized in the first medical center of Chinese PLA General Hospital from 2013 to 2017 were collected, including medical prescriptions, biochemical testing results, clinical manifestations, demographic characteristics, etc. Adaboost algorithm was used to establish the machine learning model and classify the treatment plan of the patients, with 1 697 cases as training set and 1 308 cases as testing set randomly, and then accuracy and Kappa coefficient of the model were computed.
Results The prediction accuracy of the model by Adaboost was 64.2% and the Kappa coefficient was 0.36. After analyzing the model established by Adaboost, we found that UCr, HbA1c, CK-MB, FBG, etc. were significantly related to the treatment plan selecting.
Conclusion To some extent, Adaboost algorithm is feasible and accurate in predicting hypoglycemia treatment plan.
- type 2 diabetes,
- hypoglycemic drugs,
- Adaboost algorithm,
- mulit-class classification learning

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Intelligent classification of hypoglycemia treatment plan for patients with type 2 diabetes based on ensemble learning

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