Construction of a hypoglycemia prediction model for older adults with type 2 diabetes based on random forest algorithm
10.3760/cma.j.cn211501-20230313-00623
- VernacularTitle:基于随机森林算法的老年2型糖尿病患者低血糖预测模型构建
- Author:
Ruiting ZHANG
1
;
Yu LIU
;
Aiqing HAN
;
Quanying WU
;
Jing WANG
;
Jingyi LIU
;
Xiaoyan BAI
Author Information
1. 北京中医药大学护理学院，北京 100029
- Keywords:
Diabetes mellitus, type 2;
Older adult;
Hypoglycemia;
Predictive model;
Random forest
- From:
Chinese Journal of Practical Nursing
2023;39(23):1829-1835
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct a hypoglycemia random forest prediction model for older adults with type 2 diabetes, and assess the model′s prognostication performance through internal and external verification.Methods:From August 2022 to January 2023, 300 older adults with type 2 diabetes in Beijing Hospital were selected. The demographic characteristics, medical history, laboratory tests, and other data of the patients were collected, and the data set was randomly divided into the training set and verification set in a ratio of 7∶3. The hypoglycemia prediction model for older adults with type 2 diabetes was constructed and optimized based on the random forest algorithm. The calibration curve was used to evaluate the model′s calibration, and the ROC was used to evaluate the model′s discrimination. The clinical applicability of the model was assessed by the decision curve analysis. The risk factors for hypoglycemia in the older adults were explored by prioritizing the contributions of variables in prediction. The Bootstrap method was used for internal validation, and the validation set was used for external validation.Results:Among the 300 older adults with type 2 diabetes, 128 cases (42.67%) experienced hypoglycemia within one week. The predictive contributions of risk factors in the model were ranked as follows: the number of episodes of hypoglycemia in one month, HDL-C, heart disease, diabetes knowledge and education, combination therapy, age, duration of diabetes, staple food restriction, glycosylated hemoglobin, and gender. The internal and external calibration curves of the hypoglycemia random forest model for the older adults with type 2 diabetes fluctuated around the diagonal, indicating that the calibration degree of the predictive model is good. The AUROC of internal verification was 0.823 (95% CI 0.752-0.894), the sensitivity and specificity were 0.867 and 0.698, respectively. The external verification was 0.859 (95% CI 0.817 - 0.902), and sensitivity and specificity were 0.789 and 0.804, respectively, showing that the overall discrimination of the prediction model was good. The DCA curves were far from the all-positive line and all-negative line, which indicated that the prediction model had good clinical applicability. Conclusions:The predictive effect of this model is good, and it is suitable for predicting the risk of hypoglycemia in older adults with type 2 diabetes, and it provides a reference for early hypoglycemia screening and predictive intervention for this kind of patients.
