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Explaining sleep disorders in children after earthquakes using SHAP modelling: Container City modelling

Emriye Hilal Yayan1, Feyza İnceoğlu2, Mehmet Emin Düken3

  • 1Faculty of Nursing- Department of Child Health and Diseases Nursing, Inönü University, Malatya 44280, Turkey.

Journal of Pediatric Nursing
|May 12, 2026
PubMed

Insights

Post-traumatic sleep disorders are common in children after disasters. Machine learning models, particularly XGBoost, accurately predict these sleep issues, identifying "sleep problem situation" as a key factor.

Area of Science:

  • Pediatric Psychology
  • Computational Psychiatry
  • Disaster Medicine

Background:

  • Sleep disturbances and trauma are prevalent in children post-event.
  • Container cities present unique challenges for child well-being after disasters.

Purpose of the Study:

  • To predict factors influencing post-traumatic sleep disorders in children using machine learning.
  • To identify key predictors for sleep problems in children residing in post-disaster container settlements.

Main Methods:

  • A descriptive, cross-sectional study involving 1085 children in container cities.
  • Utilized logistic regression, random forest, and XGBoost models for prediction.
  • Assessed sleep disorders in 55.3% of the participants.

Main Results:

  • The XGBoost model demonstrated superior performance with 0.89 accuracy.
  • Achieved an F1 score of 0.87, precision of 0.90, recall of 0.85, and NPV of 0.89.
  • 'Sleep problem situation' was the most significant predictor.

Conclusions:

  • Machine learning effectively predicts childhood sleep disorders post-trauma.
  • Findings inform rehabilitation strategies and highlight the need for integrated psychiatric and pediatric nursing interventions.
  • Shelter problems during earthquakes were less influential on sleep disorders.
Abstract

Keywords:
Boosting machine learning algorithmsChildLogistic regressionMachine learningRandom forestSleep wake disorders

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