Predictive Modelling of Postnatal Depression and Infant Bonding Outcomes from Self-Reported Maternal Health Data

Postnatal depression (PND) is a critical mental health issue that affects approximately one in seven women and has a profound impact on infant bonding and family dynamics. Despite its prevalence, PND often goes undiagnosed as a result of ignorance and stigma. This study uses machine learning techniques to predict PND, offering a novel approach to early detection and intervention. Employing multilayer perceptron (MLP), decision tree, XGBoost, support vector machine (SVM), and logistic regression and KNeighbors, this research aimed to analyze various demographic, medical, and psychosocial factors to identify patterns and predictors of PND. The data was subjected to rigorous model evaluation, where the MLPClassifier emerged as the superior model with an AUC of 0.99, indicating near-perfect predictive capability. The study also explored the model’s practical application by integrating it into a web interface using Streamlit, facilitating real-time prediction and analysis of PND risk, thereby making strides toward enhancing maternal mental health care. The findings of this study not only augment the existing body of literature on PND prediction but also highlight the role of ML as an indispensable tool in advancing healthcare technology. By achieving a high level of accuracy in the prediction of PND, the MLPClassifier model has the potential to significantly improve clinical outcomes for mothers and their families.