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

Author

Listed:
  • Wei Gao

    (Shanghai General Hospital of Nanjing Medical University
    Shanghai Jiao Tong University)

  • Wuping Bao

    (Shanghai General Hospital of Nanjing Medical University
    Shanghai Jiao Tong University)

  • Xin Zhou

    (Shanghai General Hospital of Nanjing Medical University
    Shanghai Jiao Tong University)

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.

Suggested Citation

  • Wei Gao & Wuping Bao & Xin Zhou, 2019. "Analysis of cough detection index based on decision tree and support vector machine," Journal of Combinatorial Optimization, Springer, vol. 37(1), pages 375-384, January.
    • Handle: RePEc:spr:jcomop:v:37:y:2019:i:1:d:10.1007_s10878-017-0236-8
    DOI: 10.1007/s10878-017-0236-8
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    5. Yanqin Bai & Xiao Han & Tong Chen & Hua Yu, 2015. "Quadratic kernel-free least squares support vector machine for target diseases classification," Journal of Combinatorial Optimization, Springer, vol. 30(4), pages 850-870, November.
    6. Mengzhuo Bai & Chunyang Ren & Yang Liu, 2015. "A note of reduced dimension optimization algorithm of assignment problem," Journal of Combinatorial Optimization, Springer, vol. 30(4), pages 841-849, November.
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    Cited by:

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    2. Shi Yin & Jian Chang & Hailan Pan & Haizhou Mao & Mei Wang, 0. "Early warning of venous thromboembolism after surgery based on self-organizing competitive network," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-19.
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    6. Bin Li & Qianghua Wei & Xinye Zhou, 2021. "Research on model and algorithm of TCM constitution identification based on artificial intelligence," Journal of Combinatorial Optimization, Springer, vol. 42(4), pages 988-1003, November.
    7. Shi Yin & Jian Chang & Hailan Pan & Haizhou Mao & Mei Wang, 2021. "Early warning of venous thromboembolism after surgery based on self-organizing competitive network," Journal of Combinatorial Optimization, Springer, vol. 42(4), pages 909-927, November.
    8. Gang Du & Xi Liang & Xiaoling Ouyang & Chunming Wang, 0. "Risk prediction of hypertension complications based on the intelligent algorithm optimized Bayesian network," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-22.
    9. He Huang & Wei Gao & Chunming Ye, 0. "An intelligent data-driven model for disease diagnosis based on machine learning theory," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-12.
    10. Gang Du & Xi Liang & Xiaoling Ouyang & Chunming Wang, 2021. "Risk prediction of hypertension complications based on the intelligent algorithm optimized Bayesian network," Journal of Combinatorial Optimization, Springer, vol. 42(4), pages 966-987, November.
    11. He Huang & Po-Chou Shih & Yuelan Zhu & Wei Gao, 2022. "An integrated model for medical expense system optimization during diagnosis process based on artificial intelligence algorithm," Journal of Combinatorial Optimization, Springer, vol. 44(4), pages 2515-2532, November.
    12. Ruiping Wang & Mei Wang & Jian Chang & Zai Luo & Feng Zhang & Chen Huang, 0. "An optimized approach of venous thrombus embolism risk assessment," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-11.
    13. He Huang & Wei Gao & Chunming Ye, 2021. "An intelligent data-driven model for disease diagnosis based on machine learning theory," Journal of Combinatorial Optimization, Springer, vol. 42(4), pages 884-895, November.

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