Cited By
View all- Keltch BLin YBayrak C(2014)Comparison of AI Techniques for Prediction of Liver Fibrosis in Hepatitis PatientsJournal of Medical Systems10.1007/s10916-014-0060-y38:8(1-8)Online publication date: 1-Aug-2014
Adaptive splitting and selection method for noninvasive recognition of liver fibrosis stage
Pages 215 - 224
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.
References
[1]
Alpaydin, E.: Combined 5 × 2 cv f test for comparing supervised classification learning algorithms. Neural Computation 11(8), 1885-1892 (1999)
[2]
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)
[3]
Bi, Y.: The impact of diversity on the accuracy of evidential classifier ensembles. International Journal of Approximate Reasoning 53(4), 584-607 (2012)
[4]
BioPredictive. Website, http://www.biopredictive.com/intl/physician/fibrotest-for-hcv/view?set_language=en
[5]
Duda, R. O., Hart, P. E., Stork, D. G.: Pattern Classification, 2nd edn. Wiley, New York (2001)
[6]
Ho, T. K.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20, 832-844 (1998)
[7]
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)
[8]
Jackowski, K., Krawczyk, B., Woźniak, M.: Cost-Sensitive Splitting and Selection Method for Medical Decision Support System. In: Yin, H., Costa, J. A. F., Barreto, G. (eds.) IDEAL 2012. LNCS, vol. 7435, pp. 850-857. Springer, Heidelberg (2012)
[9]
Jackowski, K., Woźniak, M.: Algorithm of designing compound recognition system on the basis of combining classifiers with simultaneous splitting feature space into competence areas. Pattern Analysis and Applications 12(4), 415-425 (2009)
[10]
Jackowski, K., Woźniak, M.: Method of classifier selection using the genetic approach. Expert Systems 27(2), 114-128 (2010)
[11]
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)
[12]
Krawczyk, B., Woźniak, M.: Designing Cost-Sensitive Ensemble - Genetic Approach. In: Choraś, R. S. (ed.) Image Processing and Communications Challenges 3. AISC, vol. 102, pp. 227-234. Springer, Heidelberg (2011)
[13]
Krawczyk, B., Woźniak, M.: Analysis of Diversity Assurance Methods for Combined Classifiers. In: Choraś, R. S. (ed.) Image Processing and Communications Challenges 4. AISC, vol. 184, pp. 177-184. Springer, Heidelberg (2013)
[14]
Krawczyk, B., Woźniak, M., Orczyk, T., Porwik, P., Musialik, J., Błoýska-Fajfrowska, B.: Classification techniques for non-invasive recognition of liver fibrosis stage. Journal of Medical Informatics & Technologies 20, 121-127 (2012)
[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)
[16]
Liebowitz, J.: The handbook of applied expert systems. CRC Press, Boca Raton (1998)
[17]
Siemens Medical. Website, http://www.medical.siemens.com/webapp/wcs/stores/PSGenericDisplay~q_catalogId~e_-111~a_langId~e_-111~a_pageId~e_103713~a_storeId~e_10001.html
[18]
Michalewicz, Z.: Genetic algorithms + data structures = evolution programs, 3rd edn. Springer, London (1996)
[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)
[20]
Polikar, R.: Ensemble based systems in decision making. IEEE Circuits and Systems Magazine 6(3), 21-45 (2006)
[21]
R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2008)
[22]
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)
[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)
[24]
Woźniak, M., Zmyslony, M.: Designing combining classifier with trained fuser - analytical and experimental evaluation. Neural Network World 20(7), 925-934 (2010)
- Adaptive splitting and selection method for noninvasive recognition of liver fibrosis stage
Recommendations
Non-invasive quantitative diagnosis of liver fibrosis with an artificial neural network
AbstractHepatic fibrosis is the body’s response to chronic liver disorders caused by various causes. Ultrasonic examination using an intelligent algorithm is increasingly important for the diagnosis of hepatic fibrosis. The purpose of this study was to ...
Assessment of liver fibrosis in chronic hepatitis B via multimodal data
Assessing liver fibrosis with chronic hepatitis B (CHB) in patients is quite important. Some non-invasive approaches for evaluating liver fibrosis include blood tests and ultrasound elastography. How to effectively combine multiple methods to improve ...
Liver fibrosis emulation: Impact of the vascular fibrotic alterations on hemodynamics
AbstractThe liver circulatory system comprises two blood supply vascular trees (the hepatic artery and portal venous networks), microcirculation through the hepatic capillaries (the sinusoids), and a blood drainage vascular tree (the hepatic vein network)...
Graphical abstractDisplay Omitted
Highlights- A novel liver fibrosis model emulating vascular alterations it causes is proposed.
- Pulsatile blood flow and pressure, and elastic vessels are considered in the model.
- Altered sinusoids by fibrous tissue (such as stiffened or ...
Comments
Information & Contributors
Information
Published In
Sponsors
- UTM: Universiti Teknologi Malaysia
- Wrocław University of Technology
- NTTU: Nguyen Tat Thanh University
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 18 March 2013
Author Tags
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 04 Feb 2025
Other Metrics
Citations
Cited By
View all- Keltch BLin YBayrak C(2014)Comparison of AI Techniques for Prediction of Liver Fibrosis in Hepatitis PatientsJournal of Medical Systems10.1007/s10916-014-0060-y38:8(1-8)Online publication date: 1-Aug-2014