You are encouraged to attend the defence. The details of the defence and attendance information is included below:  

Date: March 4, 2025
Time: 10:00 AM to 12:00 PM (PT)

Defence mode: Hybrid 
In-Person Attendance: Senate Chambers, UNBC Prince George Campus  
Virtual Attendance: via Zoom (additional Zoom details at bottom of message) 

LINK TO JOIN: Please contact the Office of Graduate Administration for information regarding remote attendance for online defences. 

To ensure the defence proceeds with no interruptions, please mute your audio and video on entry and do not inadvertently share your screen. The meeting will be locked to entry 5 minutes after it begins: please ensure you are on time.  

Thesis entitled: PREDICTION AND ANALYSIS OF POSTPARTUM DEPRESSION WITH CHRONIC DISEASES AS RISK FACTORS

Abstract: 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.

Defence Committee:  
Chair: Dr. Shannon Freeman, University of Northern British Columbia  
Supervisor: Dr. Waqar Haque, University of Northern British Columbia  
Committee Member: Dr. Fan Jiang, University of Northern British Columbia  
Committee Member: Dr. Kafui Monu, University of Northern British Columbia  
External Examiner: Dr. Tammy Klassen-Ross, University of Northern British Columbia