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Coupling Machine Learning Models with Innovative Technology-based Screening Tool for Identifying Psychological Distress among Aboriginal Perinatal Mothers
  • Guanjin Wang
Guanjin Wang
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Abstract

Aboriginal perinatal mothers are at significant risk of mental health problems which may cause profound negative impacts although they are overall resilient. The present study aimed to couple machine learning models with an innovative and culturally-safe screening tool to build prediction models for identifying high psychological distress among Aboriginal perinatal mothers. Using a specifically designed web-based rubric for the perinatal mental health assessment, the original dataset of 179 Aboriginal mothers with 337 variables was obtained from twelve perinatal health settings at Perth metropolitan and regional centers in Western Australia between July and September 2022. After data preprocessing and feature selection, 23 variables about emotional manifestations, the problematic partner, worries about daily living, and the need for follow-up wraparound support were identified as significant predictors for the high risk of psychological distress measured by the Kessler 5 plus adaptation. The selected predictors were further used to train prediction models. Results showed that most of the chosen machine learning models achieved satisfactory results, where Random Forest and Support Vector Machine yielded the highest AUC of above 0.95, accuracy over 0.86, and F1 score above 0.87. This study demonstrates the potential of using machine learning-based models in the clinical decision-making to facilitate the Aboriginal families' healthcare and social and emotional well-being.