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Bayes Net Classifiers for Prediction of Renal Graft Status and Survival Period
Abstract:This paper presents the development of a Bayesian belief network classifier for prediction of graft status and survival period in renal transplantation using the patient profile information prior to the transplantation. The objective was to explore feasibility of developing a decision making tool for identifying the most suitable recipient among the candidate pool members. The dataset was compiled from the University of Toledo Medical Center Hospital patients as reported to the United Network Organ Sharing, and had 1228 patient records for the period covering 1987 through 2009. The Bayes net classifiers were developed using the Weka machine learning software workbench. Two separate classifiers were induced from the data set, one to predict the status of the graft as either failed or living, and a second classifier to predict the graft survival period. The classifier for graft status prediction performed very well with a prediction accuracy of 97.8% and true positive values of 0.967 and 0.988 for the living and failed classes, respectively. The second classifier to predict the graft survival period yielded a prediction accuracy of 68.2% and a true positive rate of 0.85 for the class representing those instances with kidneys failing during the first year following transplantation. Simulation results indicated that it is feasible to develop a successful Bayesian belief network classifier for prediction of graft status, but not the graft survival period, using the information in UNOS database.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1334844Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1636
 N. Hoot, "Models to Predict Survival After Liver Transplantation," M.S. thesis, Vanderbilt University , Nashville, Tennessee, USA, 2005.
 J.-H. Ahn, J.-W. Kwon and Y.-S. Lee, "Prediction of 1-year Graft Survival Rates in Kidney Transplantation: A Bayesian Network Model," in Proc. INFORMS & KORMS, Seoul, Korea, 2000, pp. 505-513.
 G. Machnicki, B. Pinsky, S. Takemoto, R. Balshaw, P. Salvalaggio, P. Buchanan, W. Irish, S. Bunnapradist, K. lentine, T. Burroughs, D. Brennan and M. Schnitzier, "Predictive Ability of Pretransplant Comorbidities to Predict Long-Term Graft Loss and Death," American Journal of Transplantation, Vol.9, pp. 494-505, 2009.
 B. Kaplan and J. Schold, "Neural networks for predicting graft survival," Nature, Vol. 5, pp. 190-193, April 2009.
 F. Shadabi, R. Cox, D. Sharma, and N. Petrovsky, "Use of Artificial Neural Networks in the Prediction of Kidney Transplant Outcomes," Lecture Notes in Artificial Intelligence, Vol. 3215, pp. 566-572, 2004.
 N. Petrovsky, S. K. Tam, V. Brusic, G. Russ, L. Socha, and V. B. Bajic, "Use of Artificial Neural Networks in Improving Renal Transplantation Outcomes," Graft, Vol. 5, Issue 1, pp. 6-13, 2002.
 I. Witten and E. Frank, Data Mining: Practical machine learning tools and techniques, 2nd Edition, Morgan Kaufmann, San Francisco, 2005.
 R. Bouckaert, Bayesian Network Classifiers in Weka, Technical Report, Department of Computer Science, Waikato University, Hamilton, NZ, 2005.