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.
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Acknowledgements
This research is supported by five projects: The project of Shanghai Shenkang hospital development center, clinical science and technology optimization (SHDC12017623); The doctoral start-up project of USST (BSQD201901); National natural science foundation of China (71840003, 71801150); The scientific and technological development project of USST (2018KJFZ043).
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Huang, H., Gao, W. & Ye, C. An intelligent data-driven model for disease diagnosis based on machine learning theory. J Comb Optim 42, 884–895 (2021). https://doi.org/10.1007/s10878-019-00495-x
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DOI: https://doi.org/10.1007/s10878-019-00495-x