Machine Learning Big Data Analysis of the Impact of Air Pollutants on Rhinitis-Related Hospital Visits
This study seeks to elucidate the intricate relationship between various air pollutants and the incidence of rhinitis in Seoul, South Korea, wherein it leveraged a vast repository of data and machine learning techniques. The dataset comprised more than 93 million hospital visits (<i>n</i>...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Toxics |
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| Online Access: | https://www.mdpi.com/2305-6304/11/8/719 |
| _version_ | 1797583157083504640 |
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| author | Soyeon Lee Changwan Hyun Minhyeok Lee |
| author_facet | Soyeon Lee Changwan Hyun Minhyeok Lee |
| author_sort | Soyeon Lee |
| collection | DOAJ |
| description | This study seeks to elucidate the intricate relationship between various air pollutants and the incidence of rhinitis in Seoul, South Korea, wherein it leveraged a vast repository of data and machine learning techniques. The dataset comprised more than 93 million hospital visits (<i>n</i> = 93,530,064) by rhinitis patients between 2013 and 2017. Daily atmospheric measurements were captured for six major pollutants: PM<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mn>10</mn></msub></semantics></math></inline-formula>, PM<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mrow><mn>2.5</mn></mrow></msub></semantics></math></inline-formula>, O<sub>3</sub>, NO<sub>2</sub>, CO, and SO<sub>2</sub>. We employed traditional correlation analyses alongside machine learning models, including the least absolute shrinkage and selection operator (LASSO), random forest (RF), and gradient boosting machine (GBM), to dissect the effects of these pollutants and the potential time lag in their symptom manifestation. Our analyses revealed that CO showed the strongest positive correlation with hospital visits across all three categories, with a notable significance in the 4-day lag analysis. NO<sub>2</sub> also exhibited a substantial positive association, particularly with outpatient visits and hospital admissions and especially in the 4-day lag analysis. Interestingly, O<sub>3</sub> demonstrated mixed results. Both PM<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mn>10</mn></msub></semantics></math></inline-formula> and PM<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mrow><mn>2.5</mn></mrow></msub></semantics></math></inline-formula> showed significant correlations with the different types of hospital visits, thus underlining their potential to exacerbate rhinitis symptoms. This study thus underscores the deleterious impacts of air pollution on respiratory health, thereby highlighting the importance of reducing pollutant levels and developing strategies to minimize rhinitis-related hospital visits. Further research considering other environmental factors and individual patient characteristics will enhance our understanding of these intricate dynamics. |
| first_indexed | 2024-03-10T23:31:53Z |
| format | Article |
| id | doaj.art-a7c7bffa87ba4c9ea6b75c392cd8f25d |
| institution | Directory Open Access Journal |
| issn | 2305-6304 |
| language | English |
| last_indexed | 2024-03-10T23:31:53Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Toxics |
| spelling | doaj.art-a7c7bffa87ba4c9ea6b75c392cd8f25d2023-11-19T03:15:05ZengMDPI AGToxics2305-63042023-08-0111871910.3390/toxics11080719Machine Learning Big Data Analysis of the Impact of Air Pollutants on Rhinitis-Related Hospital VisitsSoyeon Lee0Changwan Hyun1Minhyeok Lee2School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of KoreaDepartment of Urology, Korea University College of Medicine, Seoul 02841, Republic of KoreaSchool of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of KoreaThis study seeks to elucidate the intricate relationship between various air pollutants and the incidence of rhinitis in Seoul, South Korea, wherein it leveraged a vast repository of data and machine learning techniques. The dataset comprised more than 93 million hospital visits (<i>n</i> = 93,530,064) by rhinitis patients between 2013 and 2017. Daily atmospheric measurements were captured for six major pollutants: PM<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mn>10</mn></msub></semantics></math></inline-formula>, PM<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mrow><mn>2.5</mn></mrow></msub></semantics></math></inline-formula>, O<sub>3</sub>, NO<sub>2</sub>, CO, and SO<sub>2</sub>. We employed traditional correlation analyses alongside machine learning models, including the least absolute shrinkage and selection operator (LASSO), random forest (RF), and gradient boosting machine (GBM), to dissect the effects of these pollutants and the potential time lag in their symptom manifestation. Our analyses revealed that CO showed the strongest positive correlation with hospital visits across all three categories, with a notable significance in the 4-day lag analysis. NO<sub>2</sub> also exhibited a substantial positive association, particularly with outpatient visits and hospital admissions and especially in the 4-day lag analysis. Interestingly, O<sub>3</sub> demonstrated mixed results. Both PM<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mn>10</mn></msub></semantics></math></inline-formula> and PM<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mrow><mn>2.5</mn></mrow></msub></semantics></math></inline-formula> showed significant correlations with the different types of hospital visits, thus underlining their potential to exacerbate rhinitis symptoms. This study thus underscores the deleterious impacts of air pollution on respiratory health, thereby highlighting the importance of reducing pollutant levels and developing strategies to minimize rhinitis-related hospital visits. Further research considering other environmental factors and individual patient characteristics will enhance our understanding of these intricate dynamics.https://www.mdpi.com/2305-6304/11/8/719rhinitisair pollutionmachine learninghospital visitscarbon monoxidenitrogen dioxide |
| spellingShingle | Soyeon Lee Changwan Hyun Minhyeok Lee Machine Learning Big Data Analysis of the Impact of Air Pollutants on Rhinitis-Related Hospital Visits Toxics rhinitis air pollution machine learning hospital visits carbon monoxide nitrogen dioxide |
| title | Machine Learning Big Data Analysis of the Impact of Air Pollutants on Rhinitis-Related Hospital Visits |
| title_full | Machine Learning Big Data Analysis of the Impact of Air Pollutants on Rhinitis-Related Hospital Visits |
| title_fullStr | Machine Learning Big Data Analysis of the Impact of Air Pollutants on Rhinitis-Related Hospital Visits |
| title_full_unstemmed | Machine Learning Big Data Analysis of the Impact of Air Pollutants on Rhinitis-Related Hospital Visits |
| title_short | Machine Learning Big Data Analysis of the Impact of Air Pollutants on Rhinitis-Related Hospital Visits |
| title_sort | machine learning big data analysis of the impact of air pollutants on rhinitis related hospital visits |
| topic | rhinitis air pollution machine learning hospital visits carbon monoxide nitrogen dioxide |
| url | https://www.mdpi.com/2305-6304/11/8/719 |
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