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Journal of Combinatorial Optimization

, Volume 37, Issue 1, pp 375–384 | Cite as

Analysis of cough detection index based on decision tree and support vector machine

  • Wei Gao
  • Wuping Bao
  • Xin ZhouEmail author
Article
  • 89 Downloads

Abstract

In clinical medicine, cough is a common disease. Years of cough diagnosis have been collected from a large number of patients as test data. Using these test data, it is possible to find the hidden rules inside these data, which can improve the diagnosis accuracy of cough. In recent years, these related problems have been concerned by the relevant medical staff. From the known medical data, medical data mining and processing can extract knowledge, and summarize the experiences of medical experts. This technology is becoming more and more important in the medical information field. In this paper, cough test attributes ,such as peak expiratory flow and fractional exhaled nitric oxide (FENO), are modeled by decision tree and support vector machine. The experimental results show that FENO and percentage of eosinophils have a great effect on the diagnosis of cough, which are important attributes for cough diagnosis.

Keywords

Cough detection Decision tree Support vector machine 

Notes

Acknowledgements

This research was supported by the National Natural Science Foundation of China under Grant Number 81470218.

References

  1. Bai M, Ren C, Liu Y (2015) A note of reduced dimension optimization algorithm of assignment problem. J Comb Optim 30:841–849MathSciNetCrossRefzbMATHGoogle Scholar
  2. Bai Y, Han X, Chen T, Yu H (2015) Quadratic kernel-free least squares support vector machine for target diseases classification. J Comb Optim 30:850–870MathSciNetCrossRefzbMATHGoogle Scholar
  3. Gu Y, M G, Chen Q, RD S, Tang G (2013) A new two-party bargaining mechanism. J Comb Optim 25:135–163MathSciNetCrossRefzbMATHGoogle Scholar
  4. Wang S, Su H, Wan G (2015) Resource-constrained machine scheduling with machine eligibility restriction and its applications to surgical operations scheduling. J Comb Optim 30:982–995MathSciNetCrossRefzbMATHGoogle Scholar
  5. Yan J, Cheng W, Wang C, Liu J, Gao M, Zhou A (2015) Optimizing word set coverage for multi-event summarization. J Comb Optim 30:996–1015MathSciNetCrossRefzbMATHGoogle Scholar
  6. Zhong L, Luo S, Wu L, Xu L, Yang J, Tang G (2014) A two-stage approach for surgery scheduling. J Comb Optim 27:545–556MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Respiration MedicineShanghai General Hospital of Nanjing Medical UniversityShanghaiChina
  2. 2.Department of Respiratory Medicine, Shanghai General HospitalShanghai Jiao Tong UniversityShanghaiChina

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