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Development and external validation of an explainable machine learning model for identifying depressive symptoms in
Qilong Wang1, Juan He1, Siheng Ma2
1Department of Psychiatry, Gansu Provincial People's Hospital, The First Clinical College of Gansu University of Chinese Medicine, Lanzhou, Gansu, China.
Background:
Early screening for depressive symptoms in middle-aged and older adults is important for timely risk identification. Low haemoglobin may identify a clinically accessible vulnerable subgroup in which chronic disease burden, fatigue, functional impairment, and reduced physiological reserve often coexist. However, it should be understood as a high-risk phenotype rather than a direct measure of nutritional deficiency or frailty. Evidence in this population remains limited.
Methods:
This study developed a model using data from the 2015 China Health and Retirement Longitudinal Study (CHARLS), with CHARLS 2011 and a cross-sectional cohort from Gansu Provincial People's Hospital used for temporal and external validation, respectively. Depressive symptoms were defined as a 10-item Centre for Epidemiologic Studies Depression Scale score ≥10. Candidate variables included sociodemographic, behavioural, health status, functional, cognitive, and comorbidity-related information. After least absolute shrinkage and selection operator feature selection, 16 variables were retained. Seven machine learning models were compared, and Random Forest was retained for interpretation and online tool development based on its balanced performance and interpretability.
Results:
A total of 2,022 participants were included in the development cohort, 1,095 in the temporal validation cohort, and 400 in the external validation cohort. The AUROC values of the Random Forest model were 0.791, 0.776, and 0.809, respectively, with Brier scores of 0.182, 0.193, and 0.182, indicating stable discrimination and acceptable calibration. Shapley additive explanations analysis showed that sleep time, instrumental activities of daily living score, and activities of daily living score were the most important contributors. Threshold sensitivity analysis showed that lower probability thresholds increased sensitivity/recall but reduced specificity. A prototype online assessment tool was developed for individualised estimation of depressive symptom probability.
Conclusion:
This study developed and validated an interpretable machine learning model for identifying depressive symptoms in middle-aged and older adults with low haemoglobin. The model may support early screening and risk stratification in this clinically vulnerable population. However, the classification threshold and risk-stratification cut-offs are exploratory rather than clinically validated. Further multicentre validation is warranted before broader clinical implementation.