Forecasting the Effects of Real-Time Indoor PM2.5 on Peak Expiratory Flow Rates (PEFR) of Asthmatic Children in Korea: A Deep Learning Approach

We built a deep learning algorithm to predict the deterioration of health symptoms among asthmatic children between 8–12 years of age. It is based on Peak Expiratory Flow Rates (PEFR) and indoor air pollution data, as well as meteorological data collected at their indoor residences every 2 minutes using portable monitoring devices with a low-cost sensor between November 2018 and March 2019. The PEFR results collected twice a day were matched with daily PM2.5. A personalized model has been developed to predict the peak expiratory flow rate of the next day, considering indoor air quality data including PM2.5, humidity, temperature, and CO2 level in previous days. Two models were developed incorporating Indoor Air Quality (IAQ) with the PEFR-only model. The IAQ uses the daily IAQ, and 10-minute basis IAQ in predicting the future PEFR. Recurrent Neural Networks (RNN) and Deep Neural Networks (DNN) models were trained using 4 months of linked data to predict PEFR for the next days during the study period. The 10-minute RNN model was found to predict better PEFR with a Root Mean Square Error (RMSE) of 42.5 and a Mean Absolute Percentage Error (MAPE) of 14.0, as it consolidates the cumulative effects of PM2.5 concentrations over time. The highly accurate estimation showed that indoor air quality significantly affects PEFR.