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.
 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.
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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.
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and techniques, 2nd Edition, Morgan Kaufmann, San Francisco, 2005.
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Department of Computer Science, Waikato University, Hamilton, NZ,