Machine Learning Big Data Analysis of the Impact of Air Pollutants on Rhinitis-Related Hospital Visits
Abstract
:1. Introduction
2. Materials and Methods
2.1. The Comprehensive Rhinitis Patient Visit Database in Seoul
2.2. Database of Daily Atmospheric Environmental Details
2.3. Analytical Approach: Combining Traditional Statistics and Machine Learning
2.3.1. Pearson and Spearman Correlations
2.3.2. Least Absolute Shrinkage and Selection Operator (LASSO)
2.3.3. Random Forest (RF)
2.3.4. Gradient Boosting Machine (GBM)
2.3.5. Interpreting Coefficients and Importance Measures
3. Results
3.1. Exploratory Data Analysis
3.1.1. Air Pollutants Correlations
3.1.2. Hospital Visit Correlations
3.1.3. Pollutants and Patient Visits
3.2. Analysis of Hospital Visits and Air Pollutants Using Statistical Analysis
3.2.1. Pearson Correlation Analysis
3.2.2. Spearman Correlation Analysis
3.3. Analysis of Hospital Visits and Air Pollutants Using Machine Learning Analysis
3.3.1. LASSO Analysis
3.3.2. Random Forest Analysis
3.3.3. Gradient Boosting Machine Analysis
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Lee, S.; Hyun, C.; Lee, M. Machine Learning Big Data Analysis of the Impact of Air Pollutants on Rhinitis-Related Hospital Visits. Toxics 2023, 11, 719. https://doi.org/10.3390/toxics11080719
Lee S, Hyun C, Lee M. Machine Learning Big Data Analysis of the Impact of Air Pollutants on Rhinitis-Related Hospital Visits. Toxics. 2023; 11(8):719. https://doi.org/10.3390/toxics11080719
Chicago/Turabian StyleLee, Soyeon, Changwan Hyun, and Minhyeok Lee. 2023. "Machine Learning Big Data Analysis of the Impact of Air Pollutants on Rhinitis-Related Hospital Visits" Toxics 11, no. 8: 719. https://doi.org/10.3390/toxics11080719