Gold classification of COPDGene cohort based on deep learning
Jun Ying, Joyita Dutta, Ning Guo, Lei Xia, Arkadiusz Sitek, Quanzheng Li
Abstract
This study aims to employ deep learning for the development of an automatic classifier for the severity of chronic obstructive pulmonary disease (COPD) in patients. A three-layer deep belief network (DBN) with two hidden layers and one visible layer was employed to generate a model for classification, and the model's robustness against exacerbation was analyzed. Subjects from the COPDGene cohort were staged using the GOLD 2011 guidelines. 10,300 subjects with 361 features each were included in the analysis. After feature selection and parameter optimization, the proposed classification method achieved an accuracy of 97.2% by using a 10-fold cross validation experiment. The most sensitive features as revealed by the DBN weights were consistent with the clinical consensus as per previous studies and clinical diagnosis rules. In summary, we demonstrate that the DBN is a competitive tool for exacerbation risk assessment for patients suffering from, COPD.
BibTeX
@inproceedings{icassp2016_goldclassificati,
title = {Gold classification of COPDGene cohort based on deep learning},
author = {Jun Ying and Joyita Dutta and Ning Guo and Lei Xia and Arkadiusz Sitek and Quanzheng Li},
booktitle = {ICASSP 2016},
year = {2016}
}