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Journal article
Investigating Protective and Risk Factors and Predictive Insights for Aboriginal Perinatal Mental Health: Explainable Artificial Intelligence Approach
Journal of medical Internet research, Vol.27(5), e68030
2025
PMID: 40306634
Abstract
Background:
Perinatal depression and anxiety significantly impact maternal and infant health, potentially leading to severe outcomes like preterm birth and suicide. Aboriginal women, despite their resilience, face elevated risks due to the long-term effects of colonization and cultural disruption. The Baby Coming You Ready (BCYR) model of care, centered on a digitized, holistic, strengths-based assessment, was co-designed to address these challenges. The successful BCYR pilot demonstrated its ability to replace traditional risk-based screens. However, some health professionals still overrely on psychological risk scores, often overlooking the contextual circumstances of Aboriginal mothers, their cultural strengths, and mitigating protective factors. This highlights the need for new tools to improve clinical decision-making.
Objective:
We explored different explainable artificial intelligence (XAI)–powered machine learning techniques for developing culturally informed, strengths-based predictive modeling of perinatal psychological distress among Aboriginal mothers. The model identifies and evaluates influential protective and risk factors while offering transparent explanations for AI-driven decisions.
Methods:
We used deidentified data from 293 Aboriginal mothers who participated in the BCYR program between September 2021 and June 2023 at 6 health care services in Perth and regional Western Australia. The original dataset includes variables spanning cultural strengths, protective factors, life events, worries, relationships, childhood experiences, family and domestic violence, and substance use. After applying feature selection and expert input, 20 variables were chosen as predictors. The Kessler-5 scale was used as an indicator of perinatal psychological distress. Several machine learning models, including random forest (RF), CatBoost (CB), light gradient-boosting machine (LightGBM), extreme gradient boosting (XGBoost), k-nearest neighbor (KNN), support vector machine (SVM), and explainable boosting machine (EBM), were developed and compared for predictive performance. To make the black-box model interpretable, post hoc explanation techniques including Shapley additive explanations and local interpretable model-agnostic explanations were applied.
Results:
The EBM outperformed other models (accuracy=0.849, 95% CI 0.8170-0.8814; F1-score=0.771, 95% CI 0.7169-0.8245; area under the curve=0.821, 95% CI 0.7829-0.8593) followed by RF (accuracy=0.829, 95% CI 0.7960-0.8617; F1-score=0.736, 95% CI 0.6859-0.7851; area under the curve=0.795, 95% CI 0.7581-0.8318). Explanations from EBM, Shapley additive explanations, and local interpretable model-agnostic explanations identified consistent patterns of key influential factors, including questions related to “Feeling Lonely,” “Blaming Herself,” “Makes Family Proud,” “Life Not Worth Living,” and “Managing Day-to-Day.” At the individual level, where responses are highly personal, these XAI techniques provided case-specific insights through visual representations, distinguishing between protective and risk factors and illustrating their impact on predictions.
Conclusions:
This study shows the potential of XAI-driven models to predict psychological distress in Aboriginal mothers and provide clear, human-interpretable explanations of how important factors interact and influence outcomes. These models may help health professionals make more informed, non-biased decisions in Aboriginal perinatal mental health screenings.
Details
- Title
- Investigating Protective and Risk Factors and Predictive Insights for Aboriginal Perinatal Mental Health: Explainable Artificial Intelligence Approach
- Authors/Creators
- Guanjin Wang - Murdoch University, School of Information TechnologyHachem Bennamoun - Murdoch UniversityWai Hang Kwok - Edith Cowan UniversityJenny Paola Ortega Quimbayo - Murdoch UniversityBridgette Kelly - Murdoch University, Ngangk Yira Institute for ChangeTrish Ratajczak - Murdoch University, Ngangk Yira Institute for ChangeRhonda Marriott - Murdoch University, Ngangk Yira Institute for ChangeRoz Walker - Murdoch University, Ngangk Yira Institute for ChangeJayne Kotz - Murdoch University, Ngangk Yira Institute for Change
- Publication Details
- Journal of medical Internet research, Vol.27(5), e68030
- Publisher
- JMIR Publications
- Number of pages
- 17
- Identifiers
- 991005778128907891
- Copyright
- © 2025 The Authors
- Murdoch Affiliation
- Ngangk Yira Institute for Change; School of Information Technology
- Language
- English
- Resource Type
- Journal article
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- Collaboration types
- Domestic collaboration
- Citation topics
- 1 Clinical & Life Sciences
- 1.72 Obstetrics & Gynecology
- 1.72.1072 Perinatal Mental Health
- Web Of Science research areas
- Health Care Sciences & Services
- Medical Informatics
- ESI research areas
- Clinical Medicine