TY  - JOUR
AU  - Wang, Guanjin
AU  - Bennamoun, Hachem
AU  - Kwok, Wai Hang
AU  - Quimbayo, Jenny Paola Ortega
AU  - Kelly, Bridgette
AU  - Ratajczak, Trish
AU  - Marriott, Rhonda
AU  - Walker, Roz
AU  - Kotz, Jayne
PY  - 2025
DA  - 2025/4/30
TI  - Investigating Protective and Risk Factors and Predictive Insights for Aboriginal Perinatal Mental Health: Explainable Artificial Intelligence Approach
JO  - J Med Internet Res
SP  - e68030
VL  - 27
KW  - explainable AI
KW  - perinatal mental health
KW  - AI-assisted decision-making
KW  - perinatal
KW  - mental health
KW  - artificial intelligence
KW  - predictive
KW  - depression
KW  - anxiety
KW  - maternal health
KW  - maternal
KW  - infant health
KW  - infant
KW  - Aboriginal
KW  - woman
KW  - psychological risk
KW  - mother
KW  - decision-making
KW  - decision support
KW  - machine learning
KW  - psychological distress
KW  - Aboriginal mothers
KW  - risk factors
KW  - Australia
KW  - cultural strengths
KW  - protective factors
KW  - life events
KW  - worries
KW  - relationships
KW  - childhood experiences
KW  - domestic violence
KW  - substance use
AB  - 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. 
SN  - 1438-8871
UR  - https://www.jmir.org/2025/1/e68030
UR  - https://doi.org/10.2196/68030
DO  - 10.2196/68030
ID  - info:doi/10.2196/68030
ER  -