TY - JOUR
AU - M. Wang
AU - M. Luo
AU - L. Li
AU - S. Bao
AU - F. Chen
AU - Q. Zhao
AU - J. Guo
A1 - 
AB - BACKGROUND: The prevalence of depression among older adults places a considerable strain on healthcare systems due to a shortage of psychiatrists for professional evaluations. This study reports a machine learning (ML) model to assist in screening for geriatric depression, enabling primary care practitioners to detect and respond to cases effectively and on time. METHODS: Data from the 2011-2018 National Health and Nutrition Examination Survey (NHANES) were used. To identify relevant variables for depression screening, features were selected using Least Absolute Shrinkage and Selection Operator (LASSO) regression. Synthetic Minority Over-sampling Technique (SMOTE) was applied to address class imbalance in the training set. Seven ML algorithms were employed to develop predictive screening models. Model performance was evaluated using standard ML metrics and clinically relevant impact measures, ensuring a comprehensive assessment. To improve interpretability, Shapley Additive exPlanations (SHAP) were used to visualize the contribution of each feature. RESULTS: The analysis included 3802 participants, with 933 (24.54 %) identified as having depression. Thirteen key variables were selected for model development. The Extreme Gradient Boosting (XGBoost) model demonstrated the best performance, with an accuracy of 0.82 in test set and a maximum Area Under the Curve (AUC) of 0.88 in Receiver Operating Characteristic (ROC) analysis. Sleep disorders, gender, poverty-income ratio (PIR), serum albumin levels, and segmented neutrophil count were identified as the influential predictors. CONCLUSION: The ML model developed for screening depression in older adults showed strong predictive performance and clinical applicability, supporting health workers in the early identification and management of depression.
AD - Department of Computer and Simulation Technology, Faculty of Military Health Service, Naval Medical University, Shanghai, 200433, China.; Department of Radiation Medicine, College of Naval Medicine, Naval Medical University, Shanghai, 200433, China; Department of Nuclear Medicine, Changhai Hospital, Naval Medical University, Shanghai, 200433, China.; Department of Radiation Medicine, College of Naval Medicine, Naval Medical University, Shanghai, 200433, China; Department of Plastic Surgery, Changhai Hospital, Naval Medical University, Shanghai, 200433, China.; Department of Radiation Medicine, College of Naval Medicine, Naval Medical University, Shanghai, 200433, China.; Department of Radiation Medicine, College of Naval Medicine, Naval Medical University, Shanghai, 200433, China. Electronic address: smmuguojiaming@126.com.
AN - 41284538
BT - J Affect Disord
C5 - HIT & Telehealth; Healthcare Disparities
CP - Pt A
DA - Feb 1
DO - 10.1016/j.jad.2025.120551
DP - NLM
ET - 20251024
IS - Pt A
JF - J Affect Disord
LA - eng
N2 - BACKGROUND: The prevalence of depression among older adults places a considerable strain on healthcare systems due to a shortage of psychiatrists for professional evaluations. This study reports a machine learning (ML) model to assist in screening for geriatric depression, enabling primary care practitioners to detect and respond to cases effectively and on time. METHODS: Data from the 2011-2018 National Health and Nutrition Examination Survey (NHANES) were used. To identify relevant variables for depression screening, features were selected using Least Absolute Shrinkage and Selection Operator (LASSO) regression. Synthetic Minority Over-sampling Technique (SMOTE) was applied to address class imbalance in the training set. Seven ML algorithms were employed to develop predictive screening models. Model performance was evaluated using standard ML metrics and clinically relevant impact measures, ensuring a comprehensive assessment. To improve interpretability, Shapley Additive exPlanations (SHAP) were used to visualize the contribution of each feature. RESULTS: The analysis included 3802 participants, with 933 (24.54 %) identified as having depression. Thirteen key variables were selected for model development. The Extreme Gradient Boosting (XGBoost) model demonstrated the best performance, with an accuracy of 0.82 in test set and a maximum Area Under the Curve (AUC) of 0.88 in Receiver Operating Characteristic (ROC) analysis. Sleep disorders, gender, poverty-income ratio (PIR), serum albumin levels, and segmented neutrophil count were identified as the influential predictors. CONCLUSION: The ML model developed for screening depression in older adults showed strong predictive performance and clinical applicability, supporting health workers in the early identification and management of depression.
PY - 2026
SN - 0165-0327
SP - 120551
ST - Primary-care-focused interpretable machine learning model for depression screening in geriatrics: A comparative study of multiple algorithms
T1 - Primary-care-focused interpretable machine learning model for depression screening in geriatrics: A comparative study of multiple algorithms
T2 - J Affect Disord
TI - Primary-care-focused interpretable machine learning model for depression screening in geriatrics: A comparative study of multiple algorithms
U1 - HIT & Telehealth; Healthcare Disparities
U3 - 10.1016/j.jad.2025.120551
VL - 394
VO - 0165-0327
Y1 - 2026
ER -