Bridging Technology and Healthcare: Machine Learning in Postpartum Depression Risk Assessment and a Proposed MMSM (Maternal Mind Support Module)

Postpartum depression (PPD) poses significant risks to maternal and infant well-being, making early detection crucial for effective intervention. This study investigates the potential for the clinical application of machine learning models in predicting the risk of PPD using survey data. By analysing various algorithms, including CatBoost, XGBoost, LightGBM, Neural Network, Random Forest, and AdaBoost, we aim to identify key predictors and their clinical implications. Our findings highlight that LightBGM and XGBoost provide the most accurate predictions, with significant predictors such as feelings of guilt, anxiety levels, and sleep disturbances. These insights can guide healthcare professionals in identifying at-risk individuals and implementing early intervention strategies. Furthermore, the research emphasizes the importance of integrating machine learning tools with clinical practices to enhance maternal mental health support, offering tailored therapeutic suggestions based on identified risk factors. This study underscores the vital role of early detection and personalized care in mitigating the impact of PPD.