AccScience Publishing / HPR / Online First / DOI: 10.36922/HPR026260138
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RESEARCH ARTICLE

Development and external validation of an interpretable machine learning model for depressive symptom screening in Chinese adults aged 50 years or older with diabetes

Siheng Ma1† ,  Qilong Wang2† ,  Rui Qi3 ,  Huaning Wang1 ,  Min Cai1*
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1 Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi’an, Shaanxi, China
2 Department of Psychiatry, Gansu Provincial Hospital, Gansu University of Chinese Medicine, Lanzhou, Gansu, China
3 Department of Endocrinology, Xijing Hospital, The Fourth Military Medical University, Xi’an, Shaanxi, China
†These authors contributed equally to this work.
HPR, 026260138 https://doi.org/10.36922/HPR026260138
Received: 25 June 2026 | Revised: 26 July 2026 | Accepted: 3 August 2026 | Published online: 14 August 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Background: Depressive symptoms are common among people with diabetes and may impair self-management and quality of life.

Objective: This study aimed to develop and externally validate an interpretable machine-learning model for depressive symptom screening in Chinese adults aged 50 years or older with diabetes and translate it into an online assessment tool.

Methods: Participants with diabetes from Round 4 of the China Health and Retirement Longitudinal Study (n = 2,187) were randomly divided into training and internal validation/model-selection sets at a 7:3 ratio. An independent cohort from Xijing Hospital (n = 695) was reserved for external validation. Depressive symptoms were defined as a Center for Epidemiologic Studies Depression Scale-10 (CESD-10) score of 10 or higher. Least absolute shrinkage and selection operator regression was used for feature selection; nine machine-learning algorithms were compared in the development data, and SHapley Additive exPlanations analysis interpreted the final Extreme Gradient Boosting (XGBoost) model.

Results: Sixteen predictors were retained. XGBoost achieved areas under the curve of 0.822, 0.832, and 0.812 in the training, internal validation, and external validation sets, respectively. Major contributors included sleep duration, self-perceived health status, cognitive function, activities of daily living, instrumental activities of daily living, and life satisfaction. An online assessment tool was developed.

Conclusions: The XGBoost model showed consistent discrimination but non-ideal calibration and may support concurrent preliminary classification and triage of depressive symptoms in Chinese adults aged 50 years or older with diabetes. The calculator is not diagnostic, should not replace CESD-10 administration or professional assessment when clinically indicated, and requires recalibration and prospective multicenter validation before broader clinical use.

Keywords
Diabetes
Depressive symptoms
Machine learning
External validation
XGBoost
Online assessment tool
Funding
This research was funded by the Clinical Research Program of Xijing Hospital (Grant No. XJZT24LY35), the Fourth Military Medical University (Grant No. 2023LC2306), and the Shaanxi Province Science and Technology New Star Project (Grant No. 2023KJXX-024). The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, and approval of the manuscript; or the decision to submit the manuscript for publication.
Conflict of interest
The authors declare no conflicts of interest.
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Health Psychology Research, Electronic ISSN: 2420-8124 Published by AccScience Publishing
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