Construction and validation of frailty risk prediction model for hospitalized elderly patients with type 2 diabetes based on machine learning and SHAP Explainabilility

WU Jiaqi; FANG Suzhen; LI Kai; HUANG Min; ZHANG Yanqun; LUO Xiangrong; JIA Bin; LI Binglei; XIE Cuihua

doi:10.16460/j.issn2097-6569.2025.11.007

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Journal of Nursing ›› 2025, Vol. 32 ›› Issue (11) : 7-12. DOI: 10.16460/j.issn2097-6569.2025.11.007

Construction and validation of frailty risk prediction model for hospitalized elderly patients with type 2 diabetes based on machine learning and SHAP Explainabilility

WU Jiaqi^1a,2, FANG Suzhen^1b, LI Kai^1a,2, HUANG Min^1a,2, ZHANG Yanqun^1c,2, LUO Xiangrong^1b, JIA Bin^1a,2, LI Binglei^1a,2, XIE Cuihua^1a

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Abstract

Objective To construct a frailty risk prediction model for elderly inpatients with type 2 diabetes mellitus (T2DM) and interpret the model. Methods Convenience sampling was used to select 509 elderly inpatients with T2DM from two tertiary grade-A hospitals in Guangzhou from March to August 2024. The data were analyzed using Python 3.8 statistical software and randomly divided into a training set and a validation set in a 7:3 ratio. Five machine learning algorithms were employed to develop the models, and their performance was compared to determine the optimal prediction model, which was then interpreted. Results Among the five machine learning models, the one developed by Catboost performed the best, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.755, precision of 0.660, recall of 0.433, specificity of 0.897, accuracy of 0.752, and F1 score of 0.522. The SHAP summary plot identified the top seven most important features influencing model prediction as follows: depression, age, Geriatric Nutritional Risk Index (GNRI), hemoglobin diabetes self-management level, sleep and HbA1c. Conclusion This study develops, evaluates, and explains the frailty risk prediction model for elderly inpatients with T2DM, providing a reference for early identification of high-risk individuals and the development of targeted interventions.

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machine learning / elderly type 2 diabetes mellitus / frailty / prediction model / interpretability

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WU Jiaqi, FANG Suzhen, LI Kai, HUANG Min, ZHANG Yanqun, LUO Xiangrong, JIA Bin, LI Binglei, XIE Cuihua. Construction and validation of frailty risk prediction model for hospitalized elderly patients with type 2 diabetes based on machine learning and SHAP Explainabilility[J]. Journal of Nursing. 2025, 32(11): 7-12 https://doi.org/10.16460/j.issn2097-6569.2025.11.007

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