Postpartum Depression (PPD) is a mental health condition that is a leading cause of annually
reported infanticide incidence. Many cases go underdiagnosed due to unawareness, and
prolonged untreated conditions can lead to psychosis, causing harm to themselves and the
infant. Hence, identifying the PPD risk has become crucial and has been widely studied in the
context of traditional risk factors. Only limited research has been conducted addressing chronic
diseases as the risk factor.
Predicting PPD by utilizing the power of Machine Learning (ML) algorithms can lead to timely
intervention and management of the condition. Data obtained from the Center for Disease
Control and Prevention – Pregnancy Risk Assessment Monitoring System (CDC-PRAMS) was
used for this thesis to identify the risk factors and forecast the likelihood of depression for
mothers who suffer from one or more chronic diseases using ML models. The performance
evaluation of the selected machine learning models—Support Vector Machines (SVM),
Random Forest (RF), Logistic Regression (LR), and Neural Network (NN) was assessed using
accuracy and F1-score, which ranged from 76% and 77% for NN to 89% and 88% for LR. The
impact of each key predictor identified in the SHAP analysis demonstrated close alignment
across all models and highlighted the significance of chronic disease. The results also highlight
how chronic diseases potentially interact with other common risk factors to increase the
likelihood of PPD. An interactive dashboard is created to visualize and present preprocessed
data using charts and graphs. Also, a diagnostic screening tool developed based on the trained
models demonstrates the potential of ML as a screening tool to improve diagnostic precision
and support personalized care for enhanced quality of life.
