Application of machine learning approaches to predict the impact of ambient air pollution on outpatient visits for acute respiratory infections
Science of the Total Environment
; 858, 2023.
Article
in English
| Scopus | ID: covidwho-2244539
ABSTRACT
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 patient's hospital visits for respiratory diseases, particularly Acute Respiratory Infections (ARI). Outpatient hospital visits, air pollution and meteorological parameters were collected from March 2018 to October 2021. Eight machine learning algorithms (Random Forest model, K-Nearest Neighbors regression model, Linear regression model, LASSO regression model, Decision Tree Regressor, Support Vector Regression, X.G. Boost and Deep Neural Network with 5-layers) were applied for the analysis of daily air pollutants and outpatient visits for ARI. The evaluation was done by using 5-cross-fold confirmations. The data was randomly divided into test and training data sets at a scale of 12, respectively. Results show that among the studied eight machine learning models, the Random Forest model has given the best performance with R2 = 0.606, 0.608 without lag and 1-day lag respectively on ARI patients and R2 = 0.872, 0.871 without lag and 1-day lag respectively on total patients. All eight models did not perform well with the lag effect on the ARI patient dataset but performed better on the total patient dataset. Thus, the study did not find any significant association between ARI patients and ambient air pollution due to the intermittent availability of data during the COVID-19 period. 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. © 2022 Elsevier B.V.
Air Pollutants; Air Pollution; China; COVID-19; Humans; Machine Learning; Outpatients; Particulate Matter; Respiration Disorders; Respiratory Tract Infections; Application programs; Deep neural networks; Developing countries; Forecasting; Hospitals; Learning systems; Logistic regression; Nearest neighbor search; Pulmonary diseases; Random forests; Acute respiratory infection; Ambient air pollution; Hospital visits; Learning projects; Machine learning program; Machine-learning; Random forest modeling; Random forest regression; Risk predictions; ambient air; atmospheric pollution; data set; developing world; industrialization; performance assessment; prediction; public health; respiratory disease; risk assessment; support vector machine; air pollutant; Article; Chandigarh; controlled study; coronavirus disease 2019; data availability; decision tree; deep neural network; human; k nearest neighbor; least absolute shrinkage and selection operator; linear regression analysis; meteorology; outpatient; outpatient care; random forest; respiratory tract infection; seasonal variation; x.g. boost; breathing disorder; Decision trees; ARI; Machine learning programs; Risk prediction
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Experimental Studies
/
Prognostic study
Language:
English
Journal:
Science of the Total Environment
Year:
2023
Document Type:
Article
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