This study, conducted by Liu et al., investigates how sleep disorders among pregnant women can be predicted and thus prevented. Their goal is to develop a reliable machine learning (ML) model for early prediction of pregnancy-related sleep disorders. The researchers collected data from 1,681 pregnant women in western China. Then, they used a combination of univariate analysis, multivariate logistic regression, and regularization with LASSO regression to select a subset of 10 key predictors (age, standardized gestational weight gain, gestational weeks, severity of morning sickness, pregnancy intention, pre-pregnancy health, underlying diseases, anxiety, depression, and the combined effect of anxiety and depression). Using these predictors, they trained eight different ML algorithms with 5-fold cross-validation and compared their performance. The researchers found that LightGBM achieved the highest AUC (0.718) in the test set, with an accuracy of 0.670 and specificity of 0.764. SHAP analysis revealed depression as the strongest predictor (mean |SHAP|=0.26). Overall, the researchers found that LightGBM with SHAP interpretability can use readily available clinical and questionnaire data to flag pregnant women at higher risk of sleep disorders fairly early in prenatal care. Thus, enabling clinicians to enlist interventions to mitigate sleep-related adverse outcomes and improve maternal and infant health.