A Machine-learning Approach to Forecast Aggravation Risk in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease with Clinical Indicators.

Journal: Scientific reports
Published Date: Feb 20, 2020

Abstract

Patients with chronic obstructive pulmonary disease (COPD) repeat acute exacerbations (AE). Global Initiative for Chronic Obstructive Lung Disease (GOLD) is only available for patients in stable phase. Currently, there is a lack of assessment and prediction methods for acute exacerbation of chronic obstructive pulmonary disease (AECOPD) patients during hospitalization. To enhance the monitoring and treatment of AECOPD patients, we develop a novel C5.0 decision tree classifier to predict the prognosis of AECOPD hospitalized patients with objective clinical indicators. The medical records of 410 hospitalized AECOPD patients are collected and 28 features including vital signs, medical history, comorbidities and various inflammatory indicators are selected. The overall accuracy of the proposed C5.0 decision tree classifier is 80.3% (65 out of 81 participants) with 95% Confidence Interval (CI):(0.6991, 0.8827) and Kappa 0.6054. In addition, the performance of the model constructed by C5.0 exceeds the C4.5, classification and regression tree (CART) model and the iterative dichotomiser 3 (ID3) model. The C5.0 decision tree classifier helps respiratory physicians to assess the severity of the patient early, thereby guiding the treatment strategy and improving the prognosis of patients.

Authors

  • Junfeng Peng
    School of Data and Computer Science, Sun Yat-sen University, Guangzhou, Guangdong, China.
  • Chuan Chen
    Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China.
  • Mi Zhou
    The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangzhou, China.
  • Xiaohua Xie
    School of Data and Computer Science, Sun Yat-sen University, Guangzhou, Guangdong, China.
  • Yuqi Zhou
    Sun Yat-sen University, The Third Affiliated Hospital, Guangzhou, 510640, China. zhouyuqi@mail.sysu.edu.cn.
  • Ching-Hsing Luo
    Department of Electrical Engineering, National Cheng Kung University, Tainan 70101, Taiwan; Institute of Medical Science and Technology, National Sun Yat-sen University, KaoHsiung 80424, Taiwan. Electronic address: robinluo@mail.ncku.edu.tw.

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