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
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
/
Randomized controlled trials
Language:
English
Journal:
Science of the Total Environment
Year:
2023
Document Type:
Article
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