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@article{188959,
        author = {Javeeria Fatima and Naimoonisa Begum},
        title = {OPTIMIZED XGBOOST MODEL FOR EARLY DETECTION OF POSTPARTUM DEPRESSION - A ML APPROACH},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {4950-4961},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=188959},
        abstract = {Postpartum depression (PPD) is a prevalent mental health disorder affecting new mothers worldwide, resulting from a complex interplay of emotional, social, and physiological changes following childbirth. Early detection is critical, as timely intervention can significantly improve maternal and child well-being. This study proposes a hyperparameter-optimized XGBoost classifier to accurately predict PPD risk using responses from a standardized questionnaire. The dataset comprises 1,503 participants collected through a digital survey platform (Google Forms) affiliated with a medical institution, capturing key demographic, social, and health-related factors. Extensive hyperparameter tuning was applied to optimize the XGBoost classifier, and its performance was benchmarked against ten alternative machine learning models. The optimized XGBoost demonstrated a substantial improvement in accuracy, highlighting its potential as a predictive tool for clinical applications. Model robustness was validated using k-fold cross-validation, confirming its reliability and consistency. The findings emphasize the significance of specific risk factors in PPD onset, positioning the optimized XGBoost model as an effective solution for early PPD risk assessment and prevention planning in maternal healthcare.},
        keywords = {Postpartum Depression (PPD), XGBoost, Hyperparameter Optimization, Machine Learning, Risk Prediction, Maternal Health, Predictive Modeling},
        month = {December},
        }