Construction of a frailty prediction model for elderly diabetic inpatients based on machine learning algorithms
10.3760/cma.j.cn115682-20240401-01740
- VernacularTitle:基于机器学习算法构建老年糖尿病住院患者衰弱预测模型
- Author:
Xuemei ZHENG
1
;
Jing ZHANG
1
;
Jinlong ZHENG
1
Author Information
1. 长江大学医学部，荆州 434023
- Publication Type:Journal Article
- Keywords:
Diabetes mellitus;
Aged;
Frailty;
Machine learning algorithms;
Prediction model
- From:
Chinese Journal of Modern Nursing
2025;31(3):340-346
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct a frailty prediction model for elderly diabetic inpatients based on machine learning algorithms and evaluate the predictive performance of the model, providing a basis for the early identification and prevention of frailty in elderly diabetic patients.Methods:A convenience sampling method was used to select 380 elderly diabetic inpatients from the Endocrinology Department and Geriatrics Department of two ClassⅢ Grade A hospitals in Jingzhou, admitted from March to October 2023. Binary Logistic regression analysis was used to identify the factors influencing frailty in elderly diabetic patients. The prediction models, including random forest, support vector machine, and K-nearest neighbors algorithms, were constructed using Python 3.8.2 and sklearn library functions. The accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve ( AUC) for each model were evaluated. Results:Factors such as comorbidity, polypharmacy, self-management ability of diabetes, nutritional status, 25-hydroxyvitamin D 3, duration of diabetes, and activities of daily living were identified as risk factors for frailty in elderly diabetic patients ( P<0.05). The AUC for the random forest, support vector machine, and K-nearest neighbors prediction models were 0.85, 0.83, and 0.79, respectively. Conclusions:The constructed random forest model is the optimal model, capable of effectively predicting the risk of frailty in elderly diabetic inpatients, which is beneficial for healthcare professionals to early screen high-risk populations for frailty.
