{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T06:56:32Z","timestamp":1790751392367,"version":"4.1.0"},"reference-count":35,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:00:00Z","timestamp":1790726400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Mental health morbidity among urban informal settlements-dwelling populations is shaped by a complex interplay of behavioural exposures including screen time, working hours, and sleep duration and sociodemographic vulnerability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyzed cross-sectional data from 1,600 adults recruited from 16 clusters within the urban informal settlement field practice area of a medical college's Urban Health Training Centre in Pune, India, to develop and compare ten supervised machine learning algorithms predicting poor wellbeing (WHO-5), moderate-to-severe generalized anxiety disorder (GAD-7), and moderate-to-severe insomnia (ISI); internal consistency of all three instruments was confirmed from item-level responses. Because participants were nested within 16 clusters, our primary analysis used leave-one-cluster-out (LOCO) cross-validation, in which each cluster was held out in turn as an independent test set; a conventional participant-level 80\/20 split with 10-fold cross-validation was retained as a secondary analysis. Model discrimination, calibration, and clinical utility were assessed using AUC, sensitivity, specificity, predictive values, Brier score, decision curve analysis, and DeLong's test, each with cluster-level bootstrap 95% confidence intervals. SHAP was used to interpret the best-performing model, and feature-attribution concordance was additionally assessed across the Rashomon set of near-equivalent top models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The WHO-5, GAD-7, and ISI showed acceptable-to-excellent internal consistency in this sample (Cronbach's alpha 0.730\u20130.938; McDonald's omega 0.815\u20130.953). LightGBM achieved the highest primary (LOCO-CV) AUC for all three outcomes: 0.977 (95% CI 0.970\u20130.983) for poor wellbeing, 0.987 (95% CI 0.984\u20130.992) for GAD, and 0.988 (95% CI 0.982\u20130.993) for insomnia. Several sociodemographic predictors showed unusually strong univariate associations with the outcomes, traced to a repeating between-cluster pattern in the sampling frame; this structure means that even cluster-aware validation may remain optimistic relative to a genuinely external population, a limitation we discuss in detail. SHAP-based rankings were highly concordant (Spearman\n                      <jats:italic>\u03c1<\/jats:italic>\n                      \u2009=\u20090.84\u20130.95) across the three top-performing, statistically near-equivalent models. A class-weighting sensitivity analysis improved positive predictive value for poor wellbeing from 0.478 to 0.600 with minimal AUC cost.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Gradient-boosted tree ensembles, particularly LightGBM, showed strong and internally consistent discrimination for these three outcomes, assessed using reliable instruments; however, given the between-cluster homogeneity identified in this sample, external validation in an independent population is essential before any claim of generalizable clinical utility.<\/jats:p>\n                  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