用智能优化方法提升女性性工作者抑郁风险预测准确率
Ensemble Feature Selection and Harris Hawks Optimization for Explainable Mental Health Risk Prediction in Female Sex Workers
- 融合方差分析与互信息的集成特征选择+哈里斯鹰优化逻辑回归
- 在3005人数据上达到95.78%准确率，识别出创伤相关关键因素
- 可解释性强，适合用于弱势群体心理干预与精准医疗规划
女性性工作者（FSWs）面临抑郁等重大心理健康问题，暴力、污名化和经济困境加剧其心理风险。现有机器学习模型难以捕捉该群体高维复杂的风险模式。本文提出一种混合预测模型，结合基于ANOVA与互信息的集成特征选择方法，以及经哈里斯鹰优化调整的逻辑回归，并首次将群体智能算法应用于脆弱人群的心理健康预测。通过可解释人工智能（XAI）方法解析模型决策依据。在3,005名FSW样本上，该模型表现优于传统分类器，准确率达95.78%，F1得分为95.77%，AUC为0.96，成功识别出创伤后应激、客户相关暴力及职业因素为主要抑郁诱因。本研究弥合了传统方法与机器学习之间的差距，构建了一个可解释工具，助力脆弱群体实现早期干预、循证心理社会支持与健康管理。
原文摘要 · Abstract (English)
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.
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