用可解释AI分析室内污染，精准识别个人健康风险来源。
Can Explainable AI Assess Personalized Health Risks from Indoor Air Pollution?
- 结合多种机器学习模型与可解释性技术，定位污染源。
- 99.8%准确率识别污染来源，91%预测活动与暴露水平。
- 适合关注家庭空气质量与个性化健康防护的研究者。
尽管室外空气污染广受关注，但室内空气污染的影响仍被忽视。现有研究多集中于监测，缺乏对污染源头的精准定位。本研究通过调查143名参与者，发现公众对室内污染认知有限。基于65天涵盖焚香、室内吸烟、通风不良烹饪、过度使用空调及意外纸张燃烧等多样活动的数据，构建了全面监测系统。利用聚类分析与可解释性模型（LIME、SHAP），精准识别污染源及其影响。整合决策树、随机森林、朴素贝叶斯与SVM模型，其中决策树达到99.8%的高准确率。持续24小时数据支持个性化评估，实现91%的活动与污染暴露预测准确率。
原文摘要 · Abstract (English)
Acknowledging the effects of outdoor air pollution, the literature inadequately addresses indoor air pollution's impacts. Despite daily health risks, existing research primarily focused on monitoring, lacking accuracy in pinpointing indoor pollution sources. In our research work, we thoroughly investigated the influence of indoor activities on pollution levels. A survey of 143 participants revealed limited awareness of indoor air pollution. Leveraging 65 days of diverse data encompassing activities like incense stick usage, indoor smoking, inadequately ventilated cooking, excessive AC usage, and accidental paper burning, we developed a comprehensive monitoring system. We identify pollutant sources and effects with high precision through clustering analysis and interpretability models (LIME and SHAP). Our method integrates Decision Trees, Random Forest, Naive Bayes, and SVM models, excelling at 99.8% accuracy with Decision Trees. Continuous 24-hour data allows personalized assessments for targeted pollution reduction strategies, achieving 91% accuracy in predicting activities and pollution exposure.
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