ArticleJournal of affective disorders2023
Predicting depression and anxiety of Chinese population during COVID-19 in psychological evaluation data by XGBoost.
Article in Journal of affective disorders, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Prevalence and determinants of multimorbidity in older Chinese adults: a nationwide cross-sectional study using CLASS data.BMC geriatrics · 2026Article
- Machine learning-driven identification of key risk factors for predicting depression among nurses.BMC nursing · 2025Article
- Identifying depression with mixed features: the potential value of eye-tracking features.Frontiers in neurology · 2025Article
- Distance Learning in Nursing Education during the COVID-19 Pandemic: Psychosocial Impact for the Greek Nursing Students-A Qualitative Approach.Healthcare (Basel, Switzerland) · 2023Article
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8 authors.
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Abstract
backgroundDue to the onset of sudden stress, COVID-19 has greatly impacted the incidence of depression and anxiety. However, challenges still exist in identifying high-risk groups for depression and anxiety during COVID-19. Studies have identified how resilience and social support can be employed as effective predictors of depression and anxiety. This study aims to select the best combination of variables from measures of resilience, social support, and alexithymia for predicting depression and anxiety.
methodsThe eXtreme Gradient Boosting (XGBoost
resultsThe mean accuracies achieved by three classification tasks, DE vs HC, AN vs HC, and DA vs HC, were 0.78, 0.77, and 0.89. Further, the combination of 19 selected features almost exhibited the same performance as all 56 variables (accuracies = 0.75, 0.75, and 0.86).
conclusionsResilience, social support, and some demographic data can accurately distinguish DE, AN, and DA from HC. The results can be used to inform screening practices for depression and anxiety. Additionally, the model performance of a limited scale including only 19 features indicates that using a simplified scale is feasible.
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