Copyright
©The Author(s) 2018.
World J Clin Cases. Aug 16, 2018; 6(8): 200-206
Published online Aug 16, 2018. doi: 10.12998/wjcc.v6.i8.200
Published online Aug 16, 2018. doi: 10.12998/wjcc.v6.i8.200
Table 2 The accuracy of Particulate matter machine learning for PM2.5 and PM10 concentrations to predict the events of outpatient visits for upper respiratory infections by the four regions and in all of Taiwan
Accuracy (%) | Overall population | Elderly population | |
Taiwan | PM2.5 | 81.75 | 89.05 |
PM10 | 83.21 | 88.32 | |
PM2.5 + PM10 | 83.21 | 89.05 | |
Northern region | PM2.5 | 63.04 | 80.43 |
PM10 | 73.19 | 76.81 | |
PM2.5 + PM10 | 65.94 | 78.99 | |
Central region | PM2.5 | 69.34 | 78.10 |
PM10 | 72.26 | 74.45 | |
PM2.5 + PM10 | 69.34 | 77.37 | |
Southern region | PM2.5 | 71.01 | 76.09 |
PM10 | 71.74 | 74.64 | |
PM2.5 + PM10 | 71.74 | 74.64 | |
Eastern region | PM2.5 | 67.15 | 80.29 |
PM10 | 71.53 | 81.75 | |
PM2.5 + PM10 | 71.53 | 84.67 |
- Citation: Chen MJ, Yang PH, Hsieh MT, Yeh CH, Huang CH, Yang CM, Lin GM. Machine learning to relate PM2.5 and PM10 concentrations to outpatient visits for upper respiratory tract infections in Taiwan: A nationwide analysis. World J Clin Cases 2018; 6(8): 200-206
- URL: https://www.wjgnet.com/2307-8960/full/v6/i8/200.htm
- DOI: https://dx.doi.org/10.12998/wjcc.v6.i8.200