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Machine Learning Methods for Predicting Postpartum Depression: Scoping Review
Kiran Saqib1, Amber Fozia Khan1, Zahid Ahmad Butt1
1School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada.
JMIR Mental Health
|November 25, 2021
Summary
Machine learning (ML) effectively predicts postpartum depression (PPD) using big data analytics. Further research can integrate ML into clinical practice for early PPD detection and improved maternal mental health outcomes.
Area of Science:
- Computational psychiatry
- Data science in healthcare
- Maternal mental health research
Background:
- Machine learning (ML) offers advanced analytical capabilities for predicting clinical conditions using large datasets.
- Reviewing ML and big data analytics in maternal depression is timely due to rapid technological advancements.
Purpose of the Study:
- To synthesize existing literature on ML and big data analytics for maternal mental health.
- Specifically focusing on the prediction of postpartum depression (PPD).
Main Methods:
- A scoping review using the Arksey and O'Malley framework was conducted.
- Searched PsycINFO, PubMed, IEEE Xplore, and ACM Digital Library for relevant publications from the past 12 years.
- Extracted data on ML models, data types, and study outcomes from 14 identified studies.
Main Results:
- All 14 studies utilized supervised learning techniques for PPD prediction.
- Common algorithms included Support Vector Machine (SVM), Random Forest, and XGBoost.
- Performance varied, with logistic regression achieving an AUC of 0.93 and SVM ranging from 0.78-0.86.
Conclusions:
- ML algorithms can enhance early PPD detection by analyzing large datasets and performing complex computations.
- Clinical research collaborations are needed to refine ML for PPD prediction and treatment.
- ML holds potential to become part of evidence-based practice in maternal mental healthcare.
