Adaptive splitting and selection method for noninvasive recognition of liver fibrosis stage

Authors:
Bartosz Krawczyk;Michał Woźniak;Tomasz Orczyk;Piotr Porwik
Affiliations:
Department of Systems and Computer Networks, Wroclaw University of Technology, Wroclaw, Poland;Department of Systems and Computer Networks, Wroclaw University of Technology, Wroclaw, Poland;Institute of Computer Science, University of Silesia, Sosnowiec, Poland;Institute of Computer Science, University of Silesia, Sosnowiec, Poland
Venue:
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part II
Year:
2013

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Abstract

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.