Ensemble Feature Selection and Harris Hawks Optimization for Explainable Mental Health Risk Prediction in Female Sex Workers
arXiv:2606.24047v1 Announce Type: new Abstract: One of the significant mental health issues affecting female sex workers (FSWs) is mental disorders, especially depression. Exposure to violence, stigma, and economic hardship further increases their psychological risk. Current machine learning (ML) models are typically ineffective at capturing the high-dimensional and complex risk patterns that exist in this marginalized group. This paper suggests a hybrid predictive model that merges an ensemble feature selection strategy using ANOVA and mutual information and Harris Hawks optimization-tuned lo
The proliferation of machine learning techniques allows for their application to increasingly niche and complex social issues, such as mental health in marginalized communities.
This research explores how advanced AI methods can be tailored for sensitive social issues, highlighting a growing trend in ethical AI application for vulnerable populations.
This specific paper introduces a refined methodological approach for mental health risk prediction but does not represent a significant shift in AI capabilities or societal impact.
- · AI researchers
- · Mental health professionals
- · Social workers
Improved accuracy in identifying mental health risks within specific, marginalized groups.
Potential for early intervention programs to be better targeted, leading to more effective resource allocation.
Increased trust in AI-driven tools for social welfare and public health, if these models prove robust and equitable.
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Read at arXiv cs.AI