aceDepressive Symptom Predictors in Older Mexican Adults: Interaction Structures and Nonlinear Effects from Machine Learning Explainability
Background Depression among older adults remains a critical public health issue in Latin America, where aging populations face compounded social and health vulnerabilities. Objective To characterize psychosocial and clinical predictors of depressive symptoms among older Mexican adults using LASSO-guided logistic regression, Random Forest classification, and post-hoc explainability methods. Methods A cross-sectional analysis was conducted using baseline data from a cohort (n = 1,252) of adults aged ≥60 affiliated…
Explainability methods applied to machine learning models revealed interaction structures and nonlinear effects, such as a U-shaped age pattern, that conventional regression could not detect in older Mexican adults.
The more complex Random Forest model discriminated significantly worse than logistic regression, and overall discrimination was only moderate, so predictive performance is limited.
Evidence
- Peer-reviewedArchives of Medical Research2026-10-07
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Truvace Impact Record TRV-2026-1759, v1: “Depressive Symptom Predictors in Older Mexican Adults: Interaction Structures and Nonlinear Effects from Machine Learning Explainability.” Truvace, 2026-10-18. /record/TRV-2026-1759 (accessed at citation time). sha256 324545b77dd18f4f…
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