Adaptive Splitting and Selection Method for Noninvasive Recognition of Liver Fibrosis Stage
- 1.8k Downloads
Therapy of patients suffer form liver diseases strongly depends on the liver fibrosis progression. Unfortunately, to asses it the liver biopsy has been usually used which is an invasive and raging medical procedure which could lead to serious health complications. Additionally even when experienced medical experts perform liver biopsy and read the findings, up to a 20% error rate in liver fibrosis staging has been reported. Nowadays a few noninvasive commercial tests based on the blood examinations are available for the mentioned above problem. Unfortunately they are quite expensive and usually they are not refundable by the health insurance in Poland. Thus, the cross-disciplinary team, which includes researches form the Polish medical and technical universities has started work on new noninvasive method of liver fibrosis stage classification. This paper presents a starting point of the project where several traditional classification methods are compared with the originally developed classifier ensembles based on local specialization of the classifiers in given feature space partitions. The experiment was carried out on the basis of originally acquired database about patients with the different stages of liver fibrosis. The preliminary results are very promising, because they confirmed the possibility of outperforming the noninvasive commercial tests.
Keywordsmachine learning multiple classifier system clustering and selection evolutionary algorithm feature selection medical informatics liver fibrosis
Unable to display preview. Download preview PDF.
- 4.BioPredictive. Website, http://www.biopredictive.com/intl/physician/fibrotest-for-hcv/view?set_language=en
- 14.Krawczyk, B., Woźniak, M., Orczyk, T., Porwik, P., Musialik, J., Błońska-Fajfrowska, B.: Classification techniques for non-invasive recognition of liver fibrosis stage. Journal of Medical Informatics & Technologies 20, 121–127 (2012)Google Scholar
- 15.Kuncheva, L.I.: Clustering-and-selection model for classifier combination. In: Proceedings of the Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies, vol. 1, pp. 185–188 (2000)Google Scholar
- 19.Orczyk, T., Pałys, M., Porwik, P., Musialik, J., Błońska-Fajfrowska, B.: Simple and non-invasive liver fibrosis stage prediction method. Journal of Medical Informatics & Technologies 17, 227–232 (2011)Google Scholar
- 21.R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2008)Google Scholar
- 23.Woźniak, M., Krawczyk, B.: Combined classifier based on feature space partitioning. Journal of Applied Mathematics and Computer Science 22(4) (2012) (in press) (to appear)Google Scholar
- 24.Woźniak, M., Zmyslony, M.: Designing combining classifier with trained fuser - analytical and experimental evaluation. Neural Network World 20(7), 925–934 (2010)Google Scholar