Researchers have developed a novel hybrid machine learning model to predict mental health risks, specifically depression, in female sex workers. This model integrates an ensemble feature selection strategy using ANOVA and mutual information with a logistic regression model optimized by Harris Hawks optimization. The system also incorporates explainable AI (XAI) methods to identify contributing factors to mental health predictions. When tested on a dataset of 3,005 individuals, the model achieved high performance metrics, including 95.78% accuracy, 95.77% F1 score, and 0.96 AUC, highlighting post-traumatic stress, client-related violence, and occupational factors as significant contributors to depression. AI
IMPACT This research demonstrates the potential of AI to provide tailored mental health support for vulnerable populations by identifying key risk factors.
RANK_REASON The cluster contains an academic paper detailing a new methodology for mental health risk prediction using machine learning.
- ANOVA
- client-related violence
- explainable AI
- female sex worker
- Harris Hawks optimization
- machine learning
- major depressive disorder
- mutual information
- post-traumatic stress disorder
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