Application of Machine Learning Approaches to Predict the Impact of Ambient Air Pollution on Outpatient Visits for Acute Respiratory Infections

With a remarkable increase in industrialization among fast-developing countries, air pollution is rising at an alarming rate and has become a public health concern. The study aims to examine the effect of air pollution on the patient's visits to the hospitals for respiratory diseases, particularly Acute Respiratory Infections (ARI). Outpatient hospital visits, air pollution and meteorological parameters were collected from March 2018 to October 2021. Five machine learning algorithms (Random Forest model, KNNeighbors regression model, Linear regression model, Lasso penalized linear model, Decision tree) were applied for the analysis of daily air pollutants and outpatient visits for ARI. The evaluation was done by using 10 cross-fold confirmations. The data was randomly divided into test and the training data sets at a scale of 1: 3, respectively. The study did not find any significant association between ARI patients and ambient air pollution, which may be due to the intermittent availability of data during the COVID-19 period. However, it was found that amongst the different machine learning models, the Random Forest regression model has shown the highest performance (R 2 = 0.8348) to explain the association between the number of ARI patients (dependent variable) and different air pollution parameters (independent variable). This study gives insight into developing machine learning programs for risk prediction that can be used to predict analytics for several other diseases apart from ARI, such as heart disease and other respiratory diseases.