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Donor-Recipient for Liver Transplantation Using CNN and LSTM Deep Learning Techniques

EasyChair Preprint no. 4923

5 pagesDate: January 22, 2021


Objective: Prediction of the survival of liver transplantation has been an potential role in understanding and improving the matching procedure between the recipient and graft. The allocation of organs in liver transplantation is a problem that can be resolved using deep learning techniques. The methods of allocation which included the assignment of an organ to the first patient on the waiting list without taking the characteristics of the donor and characteristics of the recipient and transplant organ were used to determine graft survival. UCI Repository data set has been used which consists of male and female liver patient records.
Methods and Materials: In order to address the problem of organ allocation, the CNN method and  comparison of LSTM which is used to evaluate model performanance for accuracy.
Conclusion: To achieve the high rate of survival of liver transplantation and selecting the attributes of donor, recipient sand transplantation.

Keyphrases: CNN, deep learning, liver transplant, LSTM

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Usha M Devi and A Marimuthu},
  title = {Donor-Recipient for Liver Transplantation Using CNN and LSTM Deep Learning Techniques},
  howpublished = {EasyChair Preprint no. 4923},

  year = {EasyChair, 2021}}
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