Ensemble Feature Selection and Harris Hawks Optimization for Explainable Mental Health Risk Prediction in Female Sex Workers

2026-06-22

AI Summary

This study addresses the limitations of existing machine learning models in effectively identifying complex mental health risk factors among female sex workers, particularly their inadequate performance in depression prediction. To overcome this, the authors propose an interpretable hybrid predictive framework that integrates ensemble feature selection—combining ANOVA and mutual information—with a logistic regression model optimized via the Harris Hawks Optimization algorithm, further enhanced by explainable artificial intelligence (XAI) techniques. This approach represents the first application of swarm intelligence optimization to mental health prediction in marginalized populations. Evaluated on a sample of 3,005 individuals, the model achieves 95.78% accuracy, 95.77% F1-score, and an AUC of 0.96, precisely identifying post-traumatic stress, client-perpetrated violence, and occupation-related factors as key drivers of depressive risk, thereby offering a transparent and reliable foundation for early intervention.

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 logistic regression and represents a new application of swarm intelligence to predict mental health in vulnerable groups. The explainable AI (XAI) methods can be used to understand the factors of trauma associated with model predictions. When applied to a group of 3,005 FSWs, it can be seen that the proposed model is more effective than traditional classifiers, with an accuracy of 95.78%, an F1 score of 95.77%, and an AUC of 0.96, and identifying post-traumatic stress, client-related violence, and occupational factors as major contributors to depression. This work bridges the gaps between conventional and ML approaches to develop an XAI tool that enables vulnerable groups to receive early assistance, evidence-based targeted psychosocial care, and health planning.

Problem

• mental health risk prediction
• female sex workers
• high-dimensional risk patterns
• depression
• vulnerable populations

Innovation

• Ensemble Feature Selection
• Harris Hawks Optimization
• Explainable AI
• Mental Health Prediction
• Swarm Intelligence