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

The paper presents a hybrid predictive model that combines ensemble feature selection using ANOVA and mutual information with Harris Hawks optimization-tuned logistic regression to predict mental health risks in female sex workers (FSWs). The model achieved an accuracy of 95.78%, an F1 score of 95.77%, and an AUC of 0.96 when tested on a dataset of 3,005 FSWs, outperforming traditional classifiers. This approach leverages explainable AI (XAI) to identify key trauma factors, enabling targeted psychosocial care and early intervention for vulnerable populations, thus advancing the application of machine learning in mental health risk assessment.