alexa Artificial Neural Networks in Prediction of Patient Survival after Liver Transplantation
ISSN: 2157-7420

Journal of Health & Medical Informatics
Open Access

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Research Article

Artificial Neural Networks in Prediction of Patient Survival after Liver Transplantation

Raji CG1* and Vinod Chandra SS2

1Department of Computer Science and Engineering, M.S University, Tirunelveli, Tamil Nadu, India

2Computer Centre, University of Kerala, Thiruvananthapuram, Kerala, India

*Corresponding Author:
Raji CG
Department of Computer Science and Engineering
M.S University, Tirunelveli, Tamil Nadu, India
Tel: 0462-2338632
E-mail: [email protected]

Received date: November 14, 2015 Accepted date: January 28, 2016 Published date: February 05, 2016

Citation: Raji CG, Chandra SSV (2016) Artificial Neural Networks in Prediction of Patient Survival after Liver Transplantation. J Health Med Inform 7:215. doi:10.4172/2157-7420.1000215

Copyright: © 2016 Raji CG, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.



Abstract The use of computer based learning models in medical domain has become a significant area of research. Organ transplantation is one of the main areas where prognosis models are being used for predicting the survival of patients. Post transplantation mortality rate is reduced if there exists an intelligent system that can pick out the correct donorrecipients pairs from a pool of donor and recipient data. In this paper, we propose a survival prediction model to define three month mortality of patients after liver transplantation. We used an Artificial Neural Network model for the survival rate of liver transplantation. The data for the study was gathered from United Network for Organ Sharing transplant registry. The main objective of the study is to develop a model for short-term survival prediction of liver patients. With 10-fold cross validation we were divided the whole data into training and test data which gives an accuracy of 99.74 % by Multilayer Perceptron Artificial Neural Network model. We also compared the model with other classification models using various error performance measures. To ensure accuracy we experimented our model with existing models and proved the result.

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