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An intelligent data-driven model for disease diagnosis based on machine learning theory

Author

Listed:
  • He Huang

    (University of Shanghai for Science and Technology)

  • Wei Gao

    (Shanghai Jiaotong University)

  • Chunming Ye

    (University of Shanghai for Science and Technology)

Abstract

In the era of data, major decisions are determined by massive data, especially in the healthcare industry. In this paper, an intelligent data-driven model is proposed based on machine learning theory, specifically, support vector machine (SVM) and random forest (RF). The model is then applied to a case of disease diagnosis, cough variant asthma (CVA). The data of 137 samples with 12 attributes is collected for experiments. The results show that the proposed model achieves better prediction performance than single SVM and single RF. Besides, in order to identify the key medical indicators to enhance diagnosis accuracy and efficiency, the most important factors affecting CVA are generated by the proposed model, including FENO, EOS%, MMEF75/25, FEV1/FVC, PEF, etc. Meanwhile, it is demonstrated that the proposed model could be a user-friendly tool to improve the performance of disease diagnosis.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:jcomop:v::y::i::d:10.1007_s10878-019-00495-x
    DOI: 10.1007/s10878-019-00495-x
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    References listed on IDEAS

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    1. Lili Liu & Guochun Tang & Baoqiang Fan & Xingpeng Wang, 2015. "Two-person cooperative games on scheduling problems in outpatient pharmacy dispensing process," Journal of Combinatorial Optimization, Springer, vol. 30(4), pages 938-948, November.
    2. Peter Tsyurmasto & Michael Zabarankin & Stan Uryasev, 2014. "Value-at-risk support vector machine: stability to outliers," Journal of Combinatorial Optimization, Springer, vol. 28(1), pages 218-232, July.
    3. 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.
    4. 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.
    5. Niu Dongxiao & Ma Tiannan & Liu Bingyi, 2017. "Power load forecasting by wavelet least squares support vector machine with improved fruit fly optimization algorithm," Journal of Combinatorial Optimization, Springer, vol. 33(3), pages 1122-1143, April.
    6. Ling Gai & Jiandong Ji, 2019. "An integrated method to solve the healthcare facility layout problem under area constraints," Journal of Combinatorial Optimization, Springer, vol. 37(1), pages 95-113, January.
    7. Dimitris Bertsimas & Allison O’Hair & Stephen Relyea & John Silberholz, 2016. "An Analytics Approach to Designing Combination Chemotherapy Regimens for Cancer," Management Science, INFORMS, vol. 62(5), pages 1511-1531, May.
    8. Liwei Zhong & Yanqin Bai, 2019. "Three-sided stable matching problem with two of them as cooperative partners," Journal of Combinatorial Optimization, Springer, vol. 37(1), pages 286-292, January.
    9. Ying Yang & Bing Shen & Wei Gao & Yong Liu & Liwei Zhong, 2015. "A surgical scheduling method considering surgeons’ preferences," Journal of Combinatorial Optimization, Springer, vol. 30(4), pages 1016-1026, November.
    Full references (including those not matched with items on IDEAS)

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