Abstract
Contemporary medicine concentrates on providing high quality diagnostic services, yet it should not be forgotten that the comfort of the patient during the examination is also of high importance. Therefore non-invasive methods that allows to precisely predict the state of the disease are currently one of the key issues in the medical business. The paper presents a novel ensemble of neural networks applied to recognition of liver fibrosis stage from indirect examination method. Several neural network models are build on the basis of outputs of different feature selection algorithms. Then an ensemble pruning procedure with the usage of diversity measures is conducted in order to eliminate redundant predictors from the pool. Finally the weights of classifiers in the fusion process are assessed to establish their influence on the output of the whole ensemble. Proposed method is compared with several state-of-the-art ensemble methods. Extensive experimental investigations, carried out on a dataset collected by authors, show that the proposed method achieve a satisfactory level of the fibrosis level recognition, outperforming other machine learning algorithms and thus may be used as a real-time medical decision support system for this task.
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References
Alpaydin, E.: Combined 5 x 2 cv f test for comparing supervised classification learning algorithms. Neural Comput. 11(8), 1885–1892 (1999)
Bedossa, P., Poynard, T.: An algorithm for the grading of activity in chronic hepatitis c. the metavir cooperative study group. Hepatology 24, 289–293 (1996)
Bi, Y.: The impact of diversity on the accuracy of evidential classifier ensembles. Int. J. Approximate Reasoning 53(4), 584–607 (2012)
BioPredictive. http://www.biopredictive.com/intl/physician/fibrotest-for-hcv/view?set_language=en
Guyon, I., Gunn, S., Nikravesh, M., Zadeh L. (eds.): Feature Extraction, Foundations and Applications. Springer, Heidelberg (2006)
Hansen, L.K., Salamon, P.: Neural network ensembles. IEEE Trans. Pattern Anal. Mach. Intell. 12(10), 993–1001 (1990)
Ishak, K., Baptista, A., Bianchi, L., Callea, F., De Groote, J., Gudat, F., Denk, H., Desmet, V., Korb, G., MacSween, R.N., et al.: Histological grading and staging of chronic hepatitis. Hepatology 22, 696–699 (1995)
Kittler, J., Alkoot, F.M.: Sum versus vote fusion in multiple classifier systems. IEEE Trans. Pattern Anal. Mach. Intell. 25(1), 110–115 (2003)
Knodell, R.G., Ishak, K.G., Black, W.C., Chen, T.S., Craig, R., Kaplowitz, N., Kiernan, T.W., Wollman, J.: Formulation and application of a numerical scoring system for assessing histological activity in asymptomatic chronic active hepatitis. Hepatology 1, 431–435 (1981)
Krawczyk, B., Woźniak, M., Orczyk, T., Porwik, P., Musialik, J., Błońska-Fajfrowska, B.: Classification techniques for non-invasive recognition of liver fibrosis stage. J. Med. Inform. Technol. 20, 121–127 (2012)
Krawczyk, B., Woźniak, M.: Combining diverse one-class classifiers. In: Corchado, E., Snasel, V., Abraham, A., Wozniak, M., Grana, M., Cho, S-B. (eds.) Hybrid Artificial Intelligent Systems, volume 7209 of Lecture Notes in Computer Science, pp. 590–601. Springer, Berlin (2012)
Siemens Medical. http://www.medical.siemens.com/webapp/wcs/stores/servlet/PSGenericDisplay~q_catalogId~e_-111~a_langId~e_-111~a_pageId~e_103713~a_storeId~e_10001.htm
Orczyk, T., Pałys, M., Porwik, P., Musialik, J., Błońska-Fajfrowska, B.: Simple and non-invasive liver fibrosis stage prediction method. J. Med. Inform. Technol. 17, 227–232 (2011)
Rokach, L.: Pattern Classification Using Ensemble Methods. Series in Machine Perception and Artificial Intelligence. World Scientific Publishing, Singapore (2010)
R Development Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2008)
Ting, Kai, Wells, Jonathan, Tan, Swee, Teng, Shyh, Webb, Geoffrey: Feature-subspace aggregating: ensembles for stable and unstable learners. Mach. Learn. 82, 375–397 (2011)
Wai, C.T., Greenson, J.K., Fontana, R.J., Kalbfleisch, J.D., Marrero, J.A., Conjeevaram, H.S., Lok, A.S.: A simple noninvasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis c. Hepatology 38, 518–526 (2003)
Wolpert, DH.: The supervised learning no-free-lunch theorems. In: Proceedings of the 6th Online World Conference on Soft Computing in Industrial Applications, pp. 25–42 (2001)
Michal Wozniak. Experiments with trained and untrained fusers. In Emilio Corchado, Juan Corchado, and Ajith Abraham, editors, Innovations in Hybrid Intelligent Systems, volume 44 of Advances in Soft Computing, pages 144–150. Springer Berlin / Heidelberg, 2007.
Wozniak, Michal, Zmyslony, Marcin: Combining classifiers using trained fuser—analytical and experimental results. Neural Netw. World 13(7), 925–934 (2010)
Yu, L., Liu, H.: Feature selection for high-dimensional data: a fast correlation-based filter solution. In: Proceedings of the Twentieth International Conference on Machine Learning, vol. 2, pp. 856–863 (2003)
Yu, L., Liu, H.: Efficient feature selection via analysis of relevance and redundancy. J. Mach. Learning Res. 5, 1205–1224 (2004)
Zmyslony, M., Krawczyk, B., Wozniak, M.: Combined classifiers with neural fuser for spam detection. In: Herrero, A., Snasel, V., Abraham, A., Zelinka, I., Baruque, B., Quintin, H, Calvo, JL., Sedano, J., Corchado, E. (eds.) International Joint Conference CISIS12-ICEUTE12-SOCO12 Special Sessions, volume 189 of Advances in Intelligent Systems and Computing, pp. 245–252. Springer, Heidelberg (2012)
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Krawczyk, B., Woźniak, M., Orczyk, T., Porwik, P., Musialik, J., Błońska-Fajfrowska, B. (2014). Neural Network Ensemble Based on Feature Selection for Non-Invasive Recognition of Liver Fibrosis Stage. In: Snášel, V., Krömer, P., Köppen, M., Schaefer, G. (eds) Soft Computing in Industrial Applications. Advances in Intelligent Systems and Computing, vol 223. Springer, Cham. https://doi.org/10.1007/978-3-319-00930-8_2
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