An end stage kidney disease predictor based on an artificial neural networks ensemble

Expert Systems with Applications

Volumn 40, Issue 11, 2013, Pages 4438-4445

  Di Noia, Tommaso ^a   Ostuni, Vito Claudio ^a   Pesce, Francesco ^b,c   Binetti, Giulio ^a,d   Naso, David ^a   Schena, Francesco Paolo ^b,e   Di Sciascio, Eugenio ^a  

^a POLITECNICO DI BARI   (Italy)

^b UNIVERSITY OF BARI   (Italy)

^c NATIONAL HEART AND LUNG INSTITUTE   (United Kingdom)

^d UNIVERSITY OF TEXAS AT ARLINGTON   (United States)

^e Cancer Research Center   (Italy)

Author keywords

Clinical decision support system (CDSS); End stage kidney disease; Machine learning; Neural networks ensemble

Indexed keywords

CLINICAL DECISION SUPPORT SYSTEMS; IGA NEPHROPATHY; KIDNEY DISEASE; KIDNEY TRANSPLANTATION; MEDICAL FIELDS; NEURAL NETWORKS ENSEMBLES; RENAL REPLACEMENT THERAPIES; WEB APPLICATION;

DECISION SUPPORT SYSTEMS; DIALYSIS; LEARNING SYSTEMS;

NEURAL NETWORKS;

EID: 84876065261     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2013.01.046     Document Type: Article

References (25)

1. A. Akl, A. Ismail, and M. Ghoneim Prediction of graft survival of living-donor kidney transplantation: Nomograms or artificial neural networks? Transplantation 86 10 2008 1401 1406
2. R.A. Baeza-Yates, and B. Ribeiro-Neto Modern information retrieval 1999 Addison-Wesley Longman Publishing Co., Inc Boston, MA, USA
3. S. Barbour, and H. Reich Risk stratification of patients with IgA nephropathy American Journal of Kidney Diseases 59 2012 865 873
4. F. Berthoux, H. Mohey, B. Laurent, C. Mariat, A. Afiani, and L. Thibaudin Predicting the risk for dialysis or death in IgA nephropathy Journal of the American Society Nephrolgy 4 2011 752 761
5. M. Brier, P. Ray, and J. Klein Prediction of delayed renal allograft function using an artificial neural network Nephrology Dialysis Transplantation 18 12 2003 2655 2659
6. N.A. Diamantidis, D. Karlis, and E.A. Giakoumakis Unsupervised stratification of cross-validation for accuracy estimation Artificial Intellgence 116 1-2 2000 1 16
7. Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., 1996. Advances in knowledge discovery and data mining. American Association for Artificial Intelligence, Menlo Park, CA, USA, Ch. From data mining to knowledge discovery: an overview, pp. 1-34.
8. Fielding, R.T., 2000. Architectural styles and the design of network-based software architectures. Ph.D. thesis, aAI9980887.
9. C. Geddes, J. Fox, M. Allison, J. Boulton-Jones, and K. Simpson An artificial neural network can select patients at high risk of developing progressive IgA nephropathy more accurately than experienced nephrologists Nephrology Dialysis Transplantation 1 1998 67 71
10. M. Goto, K. Wakai, T. Kawamura, M. Ando, M. Endoh, and Y. Tomino Pa scoring system to predict renal outcome in IgA nephropathy: A nationwide 10-year prospective cohort study Nephrology Dialysis Transplantation 10 2009 3068 3074
11. L.K. Hansen, and P. Salamon Neural network ensembles IEEE Transactions on Pattern Analysis and Machine Intelligence 12 10 1990 993 1001
12. S. Haykin Neural networks: A comprehensive foundation 1999 Prentice Hall
13. M. Kantardzic Data mining: Concepts, models, methods and algorithms 2002 John Wiley & Sons, Inc New York, NY, USA
14. C. Manno, G. Strippoli, C. D'Altri, D. Torres, M. Rossini, and F. Schena A novel simpler histological classification for renal survival in IgA nephropathy: A retrospective study American Journal of Kidney Diseases 49 6 2007 763 775
15. W.S. Mcculloch, and W. Pitts A logical calculus of the ideas immanent in nervous activity Bulletin of Mathematical Biophysics 5 1943 115 133
16. M. Minsky, and S. Papert Perceptrons: An introduction to computational geometry 1969 MIT Press Cambridge, MA, USA.
17. R. Naumovic, D. Furuncic, D. Jovanovic, M. Stosovic, G. Basta-Jovanovic, and V. Lezaic Application of artificial neural networks in estimating predictive factors and therapeutic efficacy in idiopathic membranous nephropathy Biomedicine & Pharmacotherapy 64 9 2010 633 638
18. D. Nguyen, and B. Widrow Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights In Neural Networks International Joint Conference Vol. 3 1990 IEEE 21 26
19. A.O. Osofisan, O.O. Adeyemo, B.A. Sawyerr, and O. Eweje Prediction of kidney failure using artificial neural networks European Journal of Science Research 61 4 2011 487 492
20. Parmanto, B., Munro, P.W., Doyle, H.R., 1995. Improving committee diagnosis with resampling techniques. In: NIPS. pp. 882-888.
21. M.P. Perrone, and L.N. Cooper When networks disagree: Ensemble methods for hybrid neural networks 1993 Chapman and Hall pp. 126-142
22. F. Rosenblatt The perceptron: A probabilistic model for information storage and organization in the brain Psychology Review 65 1958 386 408
23. D.E. Rumelhart, G.E. Hinton, and R.J. Williams Learning representations by back-propagating errors Nature 323 1986 533 536 Oct
24. R. Sorkin A quantitative occam's razor International Journal of Theoretical Physics 22 12 1983 1091 1104
25. J. Xie, K. Kiryluk, W. Wang, Z. Wang, S. Guo, and P. Shen Predicting progression of IgA nephropathy: New clinical progression risk score PLoS One 7 6 2012