Abstract
The survival rate, living status and follow-up status, long-term and short-term prognosis, acute rejection and rejection prognosis of renal transplant recipients were analyzed by decision tree. This study introduced large sample data of kidney transplantation into the decision tree, which was a statistical model to predict the prognosis of renal transplant recipients, and realize the visual expression of the various factors affecting and prioritization, and provide reliable basis for clinical practice. The integration of the renal transplantation with the software, database and decision-making tree model can realize dynamic growth of model to realize the possibility of personalized medicine for patients.
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This research was supported by the First Hospital of Jilin University.
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Yu, Y., Wang, J., Yang, X., Wang, N. (2020). Decision-Making Analysis of Prognosis of Renal Transplant Recipient on the Base of Data Mining. In: Huang, C., Chan, YW., Yen, N. (eds) Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2019). Advances in Intelligent Systems and Computing, vol 1088. Springer, Singapore. https://doi.org/10.1007/978-981-15-1468-5_146
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