ABSTRACT
In liver transplantation, matching donor and recipient is a problem that can be solved using machine learning techniques. In this paper we consider a liver transplant dataset obtained from eleven Spanish hospitals, including the patient survival or the rejection in liver transplantation one year after it. To tackle this problem, we use a multi-objective evolutionary algorithm for training generalized radial basis functions neural networks. The obtained models provided medical experts with a mathematical value to predict survival rates allowing them to come up with a right decision according to the principles of justice, efficiency and equity.
- H. A. Abbass, R. Sarker, and C. Newton. PDE: a Pareto-frontier differential evolution approach for multi-objective optimization problems. In Proceedings of the 2001 Congress on Evolutionary Computation, volume 2, Seoul, South Korea, 2001.Google ScholarCross Ref
- M. Astion and P. Wilding. Application of neural networks to the interpretation of laboratory data in cancer diagnosis. Clin Chem, 38:34--38, 1992.Google Scholar
- A. Ben-David. Comparison of classification accuracy using Cohen's Weighted Kappa. Expert Systems with Applications, 34(2):825--832, 2008. Google ScholarDigital Library
- K. Beyer, J. Goldstein, R. Ramakrishnan, and U. Shaft. When is Nearest Neighbor meaningful? In International Conference on Database Theory, pages 217--235, 1999. Google ScholarDigital Library
- R. Caruana and A. Niculescu-Mizil. Data mining in metric space: An empirical analysis of supervised learning performance criteria. In Proceedings of the 10th International Conference in Knowledge Discovery and Data Mining, pages 69--78, Seattle, USA, 2004. Google ScholarDigital Library
- A. Castaño, F. Fernández-Navarro, C. Hervás-Martínez, P. A. Gutierrez, and M. M. García. Classification by Evolutionary Generalized Radial Basis Functions. International Journal of Hybrid Intelligent Systems, 7(1):1--10, 2010. Google ScholarDigital Library
- C.-C. Chang and C.-J. Lin. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.Google Scholar
- H.-Y. Chen, T.-A. Chen, D. Min, G. Fisher, and Y.-M. Wu. Prediction of tacrolimus blood levels by using the neural network with genetic algorithm in liver transplantation patients. Ther Drug Monit, 21:50--56, 1999.Google ScholarCross Ref
- C. A. Coello Coello, G. B. Lamont, and D. A. Van Veldhuizen. Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation). Springer, 2nd edition, September 2007. Google ScholarDigital Library
- M. Cruz-Ramírez, J. Sánchez-Monedero, F. Fernández-Navarro, J. Fernández, and C. Hervás-Martínez. Memetic Pareto differential evolutionary artificial neural networks to determine growth multi-classes in predictive microbiology. Evolutionary Intelligence, 3(3-4):187--199, 2010.Google ScholarCross Ref
- I. Dvorchik, M. Subotin, W. Marsh, J. McMichael, and J. Fung. Performance of multi-layer feedforward neural networks to predict liver transplantation outcome. Methods Inf Med, 35:12--18, 1996.Google ScholarCross Ref
- J. C. Fernández, C. Hervás, F. J. Martínez, P. A. Gutiérrez, and M. Cruz. Memetic Pareto differential evolution for designing artificial neural networks in multiclassification problems using cross-entropy versus sensitivity. In Hybrid Artificial Intelligence Systems, volume 5572, pages 433--441. Springer Berlin / Heidelberg, 2009. Google Scholar
- J. C. Fernández-Caballero, F. J. Martínez-Estudillo, C. Hervás-Martínez, and P. A. Gutiérrez. Sensitivity versus accuracy in multiclass problems using memetic pareto evolutionary neural networks. IEEE Trans. on Neural Networks, 21(5):750 --770, 2010. Google ScholarDigital Library
- F. Fernández-Navarro, C. Hervás-Martínez, J. Sánchez-Monedero, and P. A. Gutierrez. MELM-GRBF: A modified version of the Extreme Learning Machine for Generalized Radial Basis Function Neural Networks. Neurocomputing, 2010. In press.Google Scholar
- D. Francois. High dimentional Data Analisis, From Optimal Metric to Feature Selection, chapter Seeking on right metric, pages 54--55. VDM Verlag, Saarbrucken, Germany, 2008. Google ScholarDigital Library
- C. Igel and M. HÃijsken. Empirical evaluation of the improved rprop learning algorithms. Neurocomputing, 50(6):105--123, 2003.Google ScholarCross Ref
- J. MacQueen. Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, pages 281--297. U. C. Berkeley Press, 1967.Google Scholar
- S. Matis, H. Doyle, I. Marino, R. Mural, and E. Uberbacher. Use of neural networks for prediction of graft failure following liver transplantation. IEEE Symposium on Computer-Based Medical Systems, 0:133--140, 1995. Google ScholarDigital Library
- G. Molino and A. Arrigoni. Design of a computer-assisted programme supporting the selection and clinical management of patients referred for liver transplantation. Ital J Gastroenterol, 26:31--43, 1994.Google Scholar
- S. Pedersen, J. Jorgensen, and J. Pedersen. Use of neural networks to diagnose acute myocardial infarction. II. A clinical application. Clin Chem, 42:613--617, 1996.Google Scholar
- K. Prank, C. Jurgens, A. von zur Muhlen, and G. Brabant. Predictive neural networks for learning the time course of blood glucose levels from the complex interaction of counterregulatory hormones. Neural Comput, 10:941--953, 1998. Google ScholarDigital Library
- P. Sharpe, H. Solberg, K. Rootwet, and M. Yearworth. Artificial neural networks in diagnosis of thyroid function from in vitro laboratory tests. Clin Chem, 39:2248--2253, 1993.Google Scholar
- D. Sheppard, D. McPhee, C. Darke, B. Shrethra, R. Moore, A. Jurewitz, and A. Gray. Predicting cytomegalovirus disease after renal transplantation: an artificial neural network approach. Int J Med Inf, 54(1):55--76, 1999.Google ScholarCross Ref
- R. Storn and K. Price. Differential evolution. A fast and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11:341--359, 1997. Google ScholarDigital Library
- I. H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques. Data Management Systems. Morgan Kaufmann (Elsevier), 2nd edition, 2005. Google ScholarDigital Library
Index Terms
Memetic evolutionary multi-objective neural network classifier to predict graft survival in liver transplant patients
Recommendations
Predicting patient survival after liver transplantation using evolutionary multi-objective artificial neural networks
Objective: The optimal allocation of organs in liver transplantation is a problem that can be resolved using machine-learning techniques. Classical methods of allocation included the assignment of an organ to the first patient on the waiting list ...
Memetic Pareto differential evolutionary neural network used to solve an unbalanced liver transplantation problem
Donor---recipient matching constitutes a complex scenario difficult to model. The risk of subjectivity and the likelihood of falling into error must not be underestimated. Computational tools for the decision-making process in liver transplantation can ...
Use of Neural Networks for Prediction of Graft Failure following Liver Transplantation
CBMS '95: Proceedings of the Eighth Annual IEEE Symposium on Computer-Based Medical SystemsAbstract: Liver transplantation is a well-established therapeutic option for patients with end-stage liver disease. However, up to 20% of transplanted livers fail to have adequate function initially, and at least half of those will eventually fail. ...
Comments