A prediction model of air pollution and Respiratory Diseases based on Ensemble learning

• Main Authors: Lu-Wen Cheng, 程路文
• Other Authors: K. Robert Lai
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/23mv9a

碩士 === 元智大學 === 資訊工程學系 === 106 === The study aimed to determine whether there is an association between air pollutants levels and outpatient clinic visits with chronic obstructive pulmonary disease (COPD) in Taiwan. Data of air pollutant concentrations (PM2.5、PM10、SO2、NO2、CO、O3) were collected from air monitoring stations. We use a case-crossover study design and conditional logistic regression models with odds ratios (OR) and 95% confidence intervals(CI) for evaluating the associations between the air pollutant factor and COPD-associated OC visits. Analyses show the PM2.5, PM10, CO, NO2, SO2 had significant effects on COPD-associated OC visits. In colder days, a significantly greater effect on COPD-associated OC visits O3 had greater lag effects (the lag was 1， 2，4，5 days) on COPD-associated OC visits. Controlling ambient air pollution would provide benefits to COPD patients. In this study, We used XGBoost algorithm to build a prediction model of air pollution and hospital readmission for Chrome Obstructive Pulmonary Disease. Compared with Random Forest, Neural Network, C5.0, AdaBoost and SVM, it was found that the model based on the integrated learning method XGBoost algorithm produces a higher classification of this problem result.