Ensemble Feature Selection and Harris Hawks Optimization for Explainable Mental Health Risk Prediction in Female Sex Workers
Ensemble Feature Selection and Harris Hawks Optimization for Explainable Mental Health Risk Prediction in Female Sex Workers
基于集成特征选择与哈里斯鹰优化的女性性工作者心理健康风险可解释性预测
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.
摘要： 影响女性性工作者（FSWs）的重大心理健康问题之一是精神障碍，尤其是抑郁症。遭受暴力、社会污名化和经济困难进一步增加了她们的心理风险。目前的机器学习（ML）模型通常无法有效捕捉这一边缘群体中存在的高维且复杂的风险模式。
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.
本文提出了一种混合预测模型，该模型结合了使用方差分析（ANOVA）和互信息的集成特征选择策略，以及经哈里斯鹰优化（HHO）调整的逻辑回归。这是群体智能在预测弱势群体心理健康方面的一种新应用。可解释人工智能（XAI）方法可用于理解与模型预测相关的创伤因素。
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.
在对 3,005 名女性性工作者进行测试时，结果显示该模型比传统分类器更有效，准确率达到 95.78%，F1 分数为 95.77%，AUC 为 0.96。研究还确定了创伤后应激、客户相关暴力和职业因素是导致抑郁症的主要因素。这项工作弥合了传统方法与机器学习方法之间的差距，开发出一种 XAI 工具，使弱势群体能够获得早期援助、基于证据的针对性社会心理护理以及健康规划。
Paper Details:
- Authors: Ahnaf Atef Choudhury, Md. Parvej Hoque Palash, Shahriar Siddique Ayon, Ramkrishna Saha, Abdullah Al Mamun
- Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
- Submission Date: 23 Jun 2026
论文详情：
- 作者： Ahnaf Atef Choudhury, Md. Parvej Hoque Palash, Shahriar Siddique Ayon, Ramkrishna Saha, Abdullah Al Mamun
- 学科： 人工智能 (cs.AI)；机器学习 (cs.LG)
- 提交日期： 2026年6月23日