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Feature-maximum-dependency-based fusion diagnosis method for COPD

  • Youli Fang
  • Hong WangEmail author
  • Lutong Wang
  • Ruitong Di
  • Yongqiang Song
Article
  • 27 Downloads

Abstract

Chronic Obstructive Pulmonary Disease (COPD) is a chronic lung disease that causes a progressive decline in respiratory function. COPD has become the fourth most lethal disease in the world, and worldwide deaths continue to become more common as a result of COPD. Therefore, it is important to help doctors diagnose COPD more accurately using big data analytics and effective algorithms. In the past, COPD was mainly studied as follows: applying data to determine the impact of a single feature on the disease, such as the effect of FEV1/FVC (forced expiratory volume in the first second/forced vital capacity), and analyzing a case with simple models, such as logistic regression or a support vector machine. Therefore, there are obviously deficiencies in previous studies. First, the impacts of multi-dimensional features on COPD have not been considered comprehensively. Second, there is no fusion of multiple study methods on the diagnosis and prognosis of COPD. Thus, this paper presents a feature-maximum-dependency-based fusion diagnosis method for COPD. First, the MDF-RS (feature maximum dependency-rough set) algorithm is proposed to extract the optimal combination of multi-dimensional features. Second, the integrated model DSA-SVM (direct search simulated annealing-support vector machine) is presented to classify the disease. Finally, the proposed method is experimentally tested. The results show that the algorithms outperform other classic methods.

Keywords

COPD Feature-maximum-dependency Multi-dimensional feature Integrated learning 

Notes

Acknowledgements

The work is partially supported by the National Natural Science Foundation of China (Nos. 61672329, 61373149, 61472233, 61572300, 81273704), Shandong Province Science and Technology Plan Supported Project (No. 2014GGX101026) and Taishan Scholar Fund of Shandong Province (No. TSHW201502038, 20110819). We also gratefully acknowledge the support of NVIDIA Corporation with the donation of the TITAN X GPU used for this research.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Information Science and EngineeringShandong Normal UniversityJinanChina
  2. 2.Shandong Provincial Key Laboratory for Distributed Computer Software Novel TechnologyJinanChina

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