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

新AI模型预测女性性工作者心理健康风险

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-23 01:19

研究人员开发了一种新颖的混合机器学习模型，用于预测女性性工作者的心理健康风险，特别是抑郁症。该模型集成了使用ANOVA和互信息的集成特征选择策略，以及由Harris Hawks优化算法优化的逻辑回归模型。该系统还纳入了可解释AI（XAI）方法，以识别影响心理健康预测的因素。在对3,005名个体的数据集进行测试时，该模型取得了很高的性能指标，包括95.78%的准确率、95.77%的F1分数和0.96的AUC，突出了创伤后应激障碍、客户相关的暴力以及职业因素是导致抑郁症的重要原因。 AI

影响这项研究展示了AI通过识别关键风险因素，为弱势群体提供量身定制的心理健康支持的潜力。

排序理由该集群包含一篇学术论文，详细介绍了使用机器学习进行心理健康风险预测的新方法。

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报道来源 [2]

arXiv cs.AI TIER_1 English(EN) · Ahnaf Atef Choudhury, Md. Parvej Hoque Palash, Shahriar Siddique Ayon, Ramkrishna Saha, Abdullah Al Mamun · 2026-06-24 04:00

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

arXiv:2606.24047v1 Announce Type: new 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 mach…
arXiv cs.LG TIER_1 English(EN) · Abdullah Al Mamun · 2026-06-23 01:19

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

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 ineffecti…

报道来源 [2]

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

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