Predicting hospital admissions for upper respiratory tract complaints: An artificial neural network approach integrating air pollution and meteorological factors

• Published in: Environmental monitoring and assessment 2024-08, Vol.196 (8), p.759, Article 759
• Main Authors: Mutlu, Atilla, Aydın Keskin, Gülşen, Çıldır, İhsan
• Format: Article
• Language: English
• Subjects: Adolescent; Adult; Aged; Air Pollutants - analysis; Air pollution; Air Pollution - statistics & numerical data; Air quality; Algorithms; Artificial neural networks; Asthma; Atmospheric Protection/Air Quality Control/Air Pollution; Back propagation networks; Bronchitis; Child; Child, Preschool; Climatic conditions; Comparative analysis; Earth and Environmental Science; Ecology; Ecotoxicology; Environment; Environmental Management; Environmental Monitoring - methods; Female; Health policy; Hospitalization - statistics & numerical data; Hospitals; Humans; Male; Meteorological Concepts; Meteorological conditions; Middle Aged; Monitoring/Environmental Analysis; Multilayer perceptrons; Neural networks; Neural Networks, Computer; Outdoor air quality; Particulate matter; Particulate Matter - analysis; Patient admissions; Performance prediction; Pollutants; Population studies; Respiratory diseases; Respiratory disorders; Respiratory tract; Respiratory tract diseases; Respiratory Tract Diseases - epidemiology; Respiratory tract infection; Respiratory Tract Infections - epidemiology; Seasonal variations; Sinusitis; Sulfur dioxide; Sulfur Dioxide - analysis; Well being; Young Adult
• ISSN: 0167-6369; 1573-2959; 1573-2959
• DOI: 10.1007/s10661-024-12908-4

This study uses artificial neural networks (ANNs) to examine the intricate relationship between air pollutants, meteorological factors, and respiratory disorders. The study investigates the correlation between hospital admissions for respiratory diseases and the levels of PM 10 and SO 2 pollutants, as well as local meteorological conditions, using data from 2017 to 2019. The objective of this study is to clarify the impact of air pollution on the well-being of the general population, specifically focusing on respiratory ailments. An ANN called a multilayer perceptron (MLP) was used. The network was trained using the Levenberg–Marquardt (LM) backpropagation algorithm. The data revealed a substantial increase in hospital admissions for upper respiratory tract diseases, amounting to a total of 11,746 cases. There were clear seasonal fluctuations, with fall having the highest number of cases of bronchitis ( N  = 181), sinusitis ( N  = 83), and upper respiratory infections ( N  = 194). The study also found demographic differences, with females and people aged 18 to 65 years having greater admission rates. The performance of the ANN model, measured using R 2 values, demonstrated a high level of predictive accuracy. Specifically, the R 2 value was 0.91675 during training, 0.99182 during testing, and 0.95287 for validating the prediction of asthma. The comparative analysis revealed that the ANN-MLP model provided the most optimal result. The results emphasize the effectiveness of ANNs in representing the complex relationships between air quality, climatic conditions, and respiratory health. The results offer crucial insights for formulating focused healthcare policies and treatments to alleviate the detrimental impact of air pollution and meteorological factors.