Prediction of COPD severity based on clinical data using Machine Learning

  • Choi, Eun A.; 
  • Kim, Jiwon; 
  • Nah, Guemkyung; 
  • Kim, Woojin; 
  • Yoo, Kwangha; 
  • 외 2명
Citations

SCOPUS

10

초록

Given numerous efforts of genome analysis and disease prediction studies on Chronic obstructive pulmonary disease (COPD), however, there was no model for predicting COPD severity. In this study, we constructed the prediction model for COPD severity using various machine learning techniques. By analyzing 36S samples of mild and severe COPD groups, we observed that the model using random forest performed the best (AUC =0.886) and Diffusing capacity of Lung CO, modified medical research council, and age were the most important features of the model. These results would provide valuable scientific evidence for predicting COPD severity. © 2021 IEEE.

키워드

COPD; DLCO; Machine Learning; severity
제목
Prediction of COPD severity based on clinical data using Machine Learning
저자
Choi, Eun A.; Kim, Jiwon; Nah, Guemkyung; Kim, Woojin; Yoo, Kwangha; Kim, Youngyoul; Yoon, Dankyu
DOI
10.1109/BIBM52615.2021.9669887
발행일
2021
유형
Conference paper
저널명
2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
페이지
1646 ~ 1648