Abstract
Prognostic models are becoming useful in assessing the severity of illness and survival analysis in medical domain. Based on the studies, we realized that the current models used in liver transplantation prognosis seems to be less accurate. In this paper, we propose a highly improved model for predicting three month post liver transplantation survival. We performed experiments on the United Nations Organ Sharing dataset, with a 10-fold cross-validation. An accuracy of 86.95 % was observed when Radial Basis Function Artificial Neural Network model was used. Other similar methods were compared with the proposed one based on the prediction accuracy. A survival analysis study for a span of 13 years was also done by comparing with MELD an actual dataset. The reported results indicate that the proposed model is suitable for long term survival analysis after liver transplantation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Cruz-Ramírez, M., Hervás-Martínez, C., Fernandez, J.C., Briceno, J., De La Mata, M.: Predicting patient survival after liver transplantation using evolutionary multi-objective artificial neural networks. Artif. Intell. Med. 58(1), 37–49 (2013)
Doyle, H.R., Dvorchik, I., Mitchell, S., Marino, I.R., Ebert, F.H., McMichael, J., Fung, J.J.: Predicting outcomes after liver transplantation. A connectionist approach. Ann. Surg. 219(4), 408 (1994)
Doyle, H.R., Marino, I.R., Jabbour, N., Zetti, G., McMichael, J., Mitchell, S., Fung, J., Starzl, T.E.: Early death or retransplantation in adults after orthotopic liver transplantation: can outcome be predicted? Transplantation 57(7), 1028 (1994)
Hareendran, A., Chandra, V.: Artificial Intelligence and Machine Learning. PHI Learning Pvt. Ltd., Delhi (2014)
Kamath, P.S., Wiesner, R.H., Malinchoc, M., Kremers, W., Therneau, T.M., Kosberg, C.L., D’Amico, G., Dickson, E.R., Kim, W.: A model to predict survival in patients with end-stage liver disease. Hepatology 33(2), 464–470 (2001)
Khosravi, B., Pourahmad, S., Bahreini, A., Nikeghbalian, S., Mehrdad, G.: Five years survival of patients after liver transplantation and its effective factors by neural network and cox poroportional hazard regression models. Hepatitis Mon. 15(9), e25164 (2015)
Marsh, J.W., Dvorchik, I., Subotin, M., Balan, V., Rakela, J., Popechitelev, E., Subbotin, V., Casavilla, A., Carr, B.I., Fung, J.J., et al.: The prediction of risk of recurrence and time to recurrence of hepatocellular carcinoma after orthotopic liver transplantation: a pilot study. Hepatology 26(2), 444–450 (1997)
Parmanto, B., Doyle, H., et al.: Recurrent neural networks for predicting outcomes after liver transplantation: representing temporal sequence of clinical observations. Methods Arch. 40(5), 386–391 (2001)
Poller, L.: International normalized ratios (INR): the first 20 years. J. Thromb. Haemost. 2(6), 849–860 (2004)
Raji, C.G., Vinod Chandra, S.S.: Artificial neural networks in prediction of patient survival after liver transplantation. J. Health Med. Inform. 7, 215–240 (2016)
Raji, C.G., Vinod Chandra, S.S. : Graft survival prediction in liver transplantation using artificial neural network models. J. Comput. Sci. (2016)
Raji, C.G., Vinod Chandra, S.S.: Predicting the survival of graft following liver transplantation using a nonlinear model. J. Pub. Health, May 2016. Springer. ISSN: 2198-1833
Vinod Chandra, S.S., Reshmi, G.: A pre-microRNA classifier by structural and thermodynamic motifs. In: World Congress on Nature and Biologically Inspired Computing, NaBIC 2009, pp. 78–83. IEEE (2009)
Zhang, M., Yin, F., Chen, B., Li, Y.P., Yan, L.N., Wen, T.F., Li, B.: Pretransplant prediction of posttransplant survival for liver recipients with benign end-stage liver diseases: a nonlinear model. PloS One 7(3), e31256 (2012)
Vinod Chandra, S.S., Girijadevi, R., Nair, A.S., Pillai, S.S., Pillai, R.M.: MTar: a computational microRNA target prediction architecture for human transcriptome. BMC Bioinformatics, 10(S:1), 1–9 (2010). ISSN 1471-2105
Acknowledgments
We would like to express our sincere thanks to Dr. ArunKumar M.L, MS, MCh, MRCS Ed, PDF (HPB), SreeGokulam Medical College & Research Foundation, Thiruvananthapuram, India for his valuable technical support including comments and suggestions to improve the quality of the work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Raji, C.G., Vinod Chandra, S.S. (2016). Prediction and Survival Analysis of Patients After Liver Transplantation Using RBF Networks. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2016. Lecture Notes in Computer Science(), vol 9714. Springer, Cham. https://doi.org/10.1007/978-3-319-40973-3_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-40973-3_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-40972-6
Online ISBN: 978-3-319-40973-3
eBook Packages: Computer ScienceComputer Science (R0)