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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

    as
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