Ensemble Machine Learning Model for Predicting Postpartum Depression Disorder

This study explores the development of an ensemble machine learning (ML) model to predict Postpartum Depression (PPD) disorder, leveraging chi-square test driven feature selection techniques and a diverse array of ML algorithms. Initially, chi-square test is employed to select the most influential features for PPD prediction. From a pool of candidate features, nine key attributes demonstrating the highest association with PPD are identified for model input. Subsequently, eight distinct ML techniques, including K Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), XG Boost (XGB), and Artificial Neural Network (ANN), are applied to develop individual predictive models. Following the training and evaluation of individual models, an ensemble approach is adopted to combine the strengths of multiple algorithms, enhancing prediction accuracy and robustness. The ensemble model aggregates predictions from diverse ML techniques, leveraging their complementary strengths to yield more accurate and reliable predictions of PPD risk. Performance evaluation metrics, such as accuracy, precision, recall, and F1-score, are employed to assess the efficacy of the ensemble model in comparison to individual ML algorithms. The results demonstrate the effectiveness of the ensemble approach in improving prediction accuracy and generalization capability, thereby offering a valuable tool for early identification and intervention in PPD cases. This research contributes to the advancement of predictive modelling in mental health by showcasing the utility of ensemble ML techniques in PPD prediction. The findings underscore the potential of feature selection and ensemble modelling in enhancing the accuracy and effectiveness of PPD risk assessment, thereby facilitating proactive interventions to support maternal mental health.