Can Explainable AI Assess Personalized Health Risks from Indoor Air Pollution?

TL;DR

This paper presents a comprehensive system using machine learning and interpretability models to accurately identify indoor air pollution sources and assess health risks based on personalized indoor activity data.

Contribution

It introduces a novel monitoring approach combining clustering and interpretability techniques for precise indoor pollution source identification.

Findings

Decision Tree model achieves 99.8% accuracy.

Personalized activity and pollution exposure prediction at 91% accuracy.

Limited awareness of indoor air pollution among participants.

Abstract

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.