Predictive Model of Sleep Disorders in Pregnant Women Using Machine Learning and SHAP Analysis

Background Sleep disorders in pregnant women are common and can adversely affect maternal and infant health. We aimed to develop a reliable machine learning (ML) model for early prediction of sleep disorders during pregnancy to inform interventions. Methods We analyzed data from 1,681 pregnant women in western China. Logistic regression and LASSO regression were used to identify key predictors of sleep disorders. Eight ML algorithms were compared, with LightGBM selected for its superior predictive performance. SHAP analysis was employed to interpret the model and assess the impact of risk factors. Results Seven significant predictors were identified: age, morning sickness, pregnancy intention, pre-pregnancy health, underlying diseases, anxiety, and depression. LightGBM demonstrated the best performance with an AUC of 0.687, accuracy of 0.670, and specificity of 0.764. The SHAP values revealed that these factors are associated with a positive influence on the model's risk score predictions. Conclusion Our LightGBM model, with its high accuracy and interpretability, can effectively predict sleep disorders in pregnant women, potentially aiding in the development of targeted interventions to improve maternal and infant health.