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@article{193606,
        author = {TAMIL ELAKYA and Dr.K.Manikandan},
        title = {Comparative Analysis of Machine Learning Models For Predicting Postpartum Depression Risk},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {1021-1025},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=193606},
        abstract = {Postpartum depression (PPD) is a prevalent mental health concern that affects mothers after childbirth, potentially influencing maternal and infant health outcomes. The Edinburgh Postnatal Depression Scale (EPDS) is a validated self-report questionnaire commonly used for screening depressive symptoms in postpartum women [1], [3]. This study implements and compares five machine learning (ML) algorithms Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and K-Nearest Neighbors to predict the risk of postpartum depression based on EPDS responses. The dataset comprised ten questionnaire items per participant, which were preprocessed and encoded for computational analysis. Each Machine Learning model was trained and evaluated using an 80-20 train-test split, with hyperparameter tuning and cross-validation applied to optimize performance. Evaluation metrics included accuracy, precision, recall, and F1-score. Comparative visualization of results highlighted differences in model performance, demonstrating that ensemble methods, particularly Random Forest, achieved superior predictive accuracy. The findings suggest that ML-based approaches can effectively support early detection of postpartum depression, enabling timely clinical intervention and personalized maternal care [15], [18].},
        keywords = {Postpartum depression, Edinburgh Postnatal Depression Scale, Machine Learning, Classification, Predictive modeling, Risk assessment.},
        month = {March},
        }