[Severity classification of chronic obstructive pulmonary disease based on deep learning].

Journal: Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Published Date:

Abstract

In this paper, a deep learning method has been raised to build an automatic classification algorithm of severity of chronic obstructive pulmonary disease. Large sample clinical data as input feature were analyzed for their weights in classification. Through feature selection, model training, parameter optimization and model testing, a classification prediction model based on deep belief network was built to predict severity classification criteria raised by the Global Initiative for Chronic Obstructive Lung Disease (GOLD). We get accuracy over 90% in prediction for two different standardized versions of severity criteria raised in 2007 and 2011 respectively. Moreover, we also got the contribution ranking of different input features through analyzing the model coefficient matrix and confirmed that there was a certain degree of agreement between the more contributive input features and the clinical diagnostic knowledge. The validity of the deep belief network model was proved by this result. This study provides an effective solution for the application of deep learning method in automatic diagnostic decision making.

Authors

  • Jun Ying
    General Hospital of PLA, Beijing 100853, P.R.China;Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.yingjun301@sina.com.
  • Ceyuan Yang
    NO.422 Hospital of PLA, Zhanjiang 524005, P.R.China.
  • Quanzheng Li
    Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
  • Wanguo Xue
    General Hospital of PLA, Beijing 100853, P.R.China.
  • Tanshi Li
    General Hospital of PLA, Beijing 100853, P.R.China.
  • Wenzhe Cao
    School of Public Health and Emergency Management, Southern University of Science and Technology, 1088 Xueyuan Avenue, Shenzhen 518055, People's Republic of China.

Keywords

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