arXiv:2606.24047v1 Announce Type: cross 
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.

تقدم دراسة جديدة نموذجًا تنبؤيًا هجينًا يجمع بين تقنيات اختيار الميزات الجماعية، بما في ذلك ANOVA والمعلومات المتبادلة، مع الانحدار اللوجستي المحسن بواسطة صقور هاريس للتنبؤ بمخاطر الصحة النفسية لدى العاملات في الجنس (FSWs). تم اختبار هذا النموذج على عينة من 3,005 من العاملات في الجنس، وحقق دقة تصل إلى 95.78%، متفوقًا على المصنفات التقليدية.

Un nuevo estudio presenta un modelo predictivo híbrido que combina técnicas de selección de características en conjunto, incluyendo ANOVA e información mutua, con regresión logística optimizada por halcones de Harris para predecir riesgos de salud mental en trabajadoras sexuales (FSWs). Este modelo, probado en una muestra de 3,005 FSWs, logró una precisión del 95.78%, superando a los clasificadores tradicionales.

Une nouvelle étude présente un modèle prédictif hybride qui combine des techniques de sélection de caractéristiques ensemblistes, y compris l'ANOVA et l'information mutuelle, avec une régression logistique optimisée par les faucons de Harris pour prédire les risques de santé mentale chez les travailleuses du sexe (FSWs). Ce modèle, testé sur un échantillon de 3 005 FSWs, a atteint une précision de 95,78 %, surpassant les classificateurs traditionnels.

A new study presents a hybrid predictive model that combines ensemble feature selection techniques, including ANOVA and mutual information, with Harris Hawks optimization-tuned logistic regression to predict mental health risks in female sex workers (FSWs). This model, tested on a sample of 3,005 FSWs, achieved an accuracy of 95.78%, outperforming traditional classifiers.