Evaluating machine learning models for predicting postpartum depression symptoms using mississippi pregnancy risk assessment monitoring system (PRAMS) 2016-2022 data.

BACKGROUND

Postpartum depression (PPD) is a significant public health concern with profound effects on maternal well-being and infant development. Building a highly accurate predictive model for PPD symptoms using data-driven approaches can facilitate earlier screening and improve intervention outcomes.

OBJECTIVE

The objectives of this study are to evaluate the performance of machine learning (ML) models for predicting self-reported PPD symptoms, identify the most important features contributing to these predictions, and assess model performance when using a reduced set of features, all based on Mississippi PRAMS 2016-2022 data.

METHODS

After preprocessing, including handling missing values, recoding variables, feature scaling, and addressing data imbalance, the Mississippi PRAMS 2016-2022 dataset from the Mississippi State Department of Health (MSDH) was prepared for analysis. Ten ML models were initially trained using 138 input features. The models were subsequently retrained using a reduced set of important features to evaluate predictive performance and compare the results with previous studies.

RESULTS

Binary classification of PPD symptoms (screening-positive versus healthy) was performed using ten ML algorithms based on 138 multivariate features. Using the 40 most important features identified by the best-performing algorithm (gradient boosting machine (GBM)), the ten models were re-evaluated. The highest area under the curve (AUC) values were achieved by GBM (0.807), histogram-based gradient boosting machine (HistGBM) (0.797), AdaBoost (0.793), and Random Forest (RF) (0.792). The GBM model achieved a sensitivity of 0.491 and a specificity of 0.960. These results compare favorably with those reported in a previous PRAMS-based study while using nearly four times fewer input features.

CONCLUSIONS

Training ML models with a reduced set of top-ranked features improves efficiency and interpretability while minimizing the risk of overfitting. Feature selection based on the best-performing model maintained predictive performance and model efficiency, particularly for ensemble-based algorithms. These findings support the use of ML models for predicting screening-positive PPD symptoms to assist population-level screening and maternal mental health interventions.