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2016 | OriginalPaper | Chapter

Liver Fibrosis Diagnosis Support System Using Machine Learning Methods

Authors : Tomasz Orczyk, Piotr Porwik

Published in: Advanced Computing and Systems for Security

Publisher: Springer India

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Abstract

Liver fibrosis is a common disease of the European population (but not only them). It may have many backgrounds and may develop with a different rapidity—it may stay hidden for many years or rapidly develop into terminal stage called cirrhosis, where liver can no longer fulfill its function. Unfortunately, current methods of diagnosis are either connected with a potential risk for a patient and require a hospitalization or are expensive and not very accurate. This paper presents a comparative study of various feature selection algorithms combined with selected machine learning algorithms which may be used to build an advanced liver fibrosis diagnosis support system based on a nonexpensive and safe routine blood tests. Experiments carried out on a dataset collected by authors, proved usability and satisfactory accuracy of the presented algorithms.

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Literature
1.
go back to reference Wojtyniak, B., Goryński, P., Moskalewicz, B.: Sytuacja zdrowotna ludności polski i jej uwarunkowania. Technical report, Narodowy Instytut Zdrowia Publicznego-Państwowy Zakład Higieny, 2012 (in Polish) Wojtyniak, B., Goryński, P., Moskalewicz, B.: Sytuacja zdrowotna ludności polski i jej uwarunkowania. Technical report, Narodowy Instytut Zdrowia Publicznego-Państwowy Zakład Higieny, 2012 (in Polish)
2.
go back to reference Stevenson, M., Lloyd-Jones, M., Morgan, M.Y., Wong, R.: Non-invasive diagnostic assessment tools for the detection of liver fibrosis in patients with suspected alcohol-related liver disease: a systematic review and economic evaluation. Health Technol. Assess. 16(4) (2012). doi:10.3310/hta16040 Stevenson, M., Lloyd-Jones, M., Morgan, M.Y., Wong, R.: Non-invasive diagnostic assessment tools for the detection of liver fibrosis in patients with suspected alcohol-related liver disease: a systematic review and economic evaluation. Health Technol. Assess. 16(4) (2012). doi:10.​3310/​hta16040
3.
go back to reference Lucas, P.J.F., Segaar, R.W., Janssens, A.R.: HEPAR: an expert system for diagnosis of disorders of the liver and biliary tract. Liver 9, 266–275 (1989)CrossRef Lucas, P.J.F., Segaar, R.W., Janssens, A.R.: HEPAR: an expert system for diagnosis of disorders of the liver and biliary tract. Liver 9, 266–275 (1989)CrossRef
4.
go back to reference Adlassnig, K.P., Horak, W.: Development and retrospective evaluation of HEPAXPERT—I: a routinely-used expert system for interpretive analysis of hepatitis A and B serologic findings. Artif. Intell. Med. 7, 1–24 (1995)CrossRef Adlassnig, K.P., Horak, W.: Development and retrospective evaluation of HEPAXPERT—I: a routinely-used expert system for interpretive analysis of hepatitis A and B serologic findings. Artif. Intell. Med. 7, 1–24 (1995)CrossRef
5.
go back to reference Zhao, Y.K., Tsutsui, T., Endo, A., Minato, K., Takahashi, T.: Design and development of an expert system to assist diagnosis and treatment of chronic hepatitis using traditional Chinese medicine. Med. Inform. 9, 37–45 (1994)CrossRef Zhao, Y.K., Tsutsui, T., Endo, A., Minato, K., Takahashi, T.: Design and development of an expert system to assist diagnosis and treatment of chronic hepatitis using traditional Chinese medicine. Med. Inform. 9, 37–45 (1994)CrossRef
6.
go back to reference Shiomi, S., Kuroki, T., Jomura, H., Ueda, T., Ikeoka, N., Kobayashi, K., Ikeda, H., Ochi, H.: Diagnosis of chronic liver disease from liver scintiscans by fuzzy reasoning. J. Nucl. Med. 36, 593–598 (1995) Shiomi, S., Kuroki, T., Jomura, H., Ueda, T., Ikeoka, N., Kobayashi, K., Ikeda, H., Ochi, H.: Diagnosis of chronic liver disease from liver scintiscans by fuzzy reasoning. J. Nucl. Med. 36, 593–598 (1995)
7.
go back to reference 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)CrossRef 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)CrossRef
8.
go back to reference Regev, A., Berho, M., Jeffers, L., Milikowski, C., Molina, E., Pyrosopoulos, N., Feng, Z., Reddy, Z., Schiff, E.: Sampling error and intraobserver variation in liver biopsy in patients with chronic HCV infection. Am. J. Gastroenterol. 97(10), 2614–2618 (2002)CrossRef Regev, A., Berho, M., Jeffers, L., Milikowski, C., Molina, E., Pyrosopoulos, N., Feng, Z., Reddy, Z., Schiff, E.: Sampling error and intraobserver variation in liver biopsy in patients with chronic HCV infection. Am. J. Gastroenterol. 97(10), 2614–2618 (2002)CrossRef
9.
go back to reference Bedossa, P., Dargere, D., Paradis, V.: Sampling variability of liver fibrosis in chronic hepatitis C. Hepatology 38, 1449–1457 (2003)CrossRef Bedossa, P., Dargere, D., Paradis, V.: Sampling variability of liver fibrosis in chronic hepatitis C. Hepatology 38, 1449–1457 (2003)CrossRef
10.
go back to reference Doroz, R., Porwik, P.: Handwritten signature recognition with adaptive selection of behavioral features. In: Communications in Computer and Information Science (CISIM), vol. 245, pp. 128–136. Springer, Kolkata (2011) Doroz, R., Porwik, P.: Handwritten signature recognition with adaptive selection of behavioral features. In: Communications in Computer and Information Science (CISIM), vol. 245, pp. 128–136. Springer, Kolkata (2011)
11.
go back to reference Porwik, P., Doroz, R.: Self-adaptive biometric classifier working on the reduced dataset. In: Hybrid Artificial Intelligence Systems. Lecture Notes in Computer Science, vol. 8480, pp. 377–388. Springer International Publishing, New York (2014) Porwik, P., Doroz, R.: Self-adaptive biometric classifier working on the reduced dataset. In: Hybrid Artificial Intelligence Systems. Lecture Notes in Computer Science, vol. 8480, pp. 377–388. Springer International Publishing, New York (2014)
12.
go back to reference Hall, M.A.: Correlation-Based Feature Subset Selection for Machine Learning. Hamilton, New Zealand (1998) Hall, M.A.: Correlation-Based Feature Subset Selection for Machine Learning. Hamilton, New Zealand (1998)
13.
go back to reference Kira, K., Rendell, L.A.: A practical approach to feature selection. In: Ninth International Workshop on Machine Learning, pp. 249–256 (1992) Kira, K., Rendell, L.A.: A practical approach to feature selection. In: Ninth International Workshop on Machine Learning, pp. 249–256 (1992)
14.
go back to reference Kononenko, I.: Estimating attributes: analysis and extensions of RELIEF. In: European Conference on Machine Learning, pp. 171–182 (1994) Kononenko, I.: Estimating attributes: analysis and extensions of RELIEF. In: European Conference on Machine Learning, pp. 171–182 (1994)
15.
go back to reference Robnik-Sikonja, M., Kononenko, I.: An adaptation of Relief for attribute estimation in regression. In: Fourteenth International Conference on Machine Learning, pp. 296–304 (1997) Robnik-Sikonja, M., Kononenko, I.: An adaptation of Relief for attribute estimation in regression. In: Fourteenth International Conference on Machine Learning, pp. 296–304 (1997)
16.
go back to reference Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Mach. Learn. 46, 389–422 (2002)CrossRefMATH Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Mach. Learn. 46, 389–422 (2002)CrossRefMATH
17.
go back to reference Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artif. Intell. 97(1–2), 273–324 (1997)CrossRefMATH Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artif. Intell. 97(1–2), 273–324 (1997)CrossRefMATH
18.
go back to reference Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989) Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)
19.
go back to reference Orczyk, T., Porwik, P., Bernaś, M.: Medical diagnosis support system based on the ensemble of single-parameter classifiers. J. Med. Inform. Technol. 23(2014), 173–179 (2014) Orczyk, T., Porwik, P., Bernaś, M.: Medical diagnosis support system based on the ensemble of single-parameter classifiers. J. Med. Inform. Technol. 23(2014), 173–179 (2014)
20.
go back to reference Foster, K.R., Koprowski, R., Skufca, J.D.: Machine learning, medical diagnosis, and biomedical engineering research—commentary. Biomed. Eng. 13(94) (2014). doi:10.1186/1475-925X-13-94 Foster, K.R., Koprowski, R., Skufca, J.D.: Machine learning, medical diagnosis, and biomedical engineering research—commentary. Biomed. Eng. 13(94) (2014). doi:10.​1186/​1475-925X-13-94
21.
go back to reference Quinlan, R. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo (1993) Quinlan, R. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo (1993)
22.
go back to reference Aha, D., Kibler, D.: Instance-based learning algorithms. Mach. Learn. 6, 37–66 (1991)MATH Aha, D., Kibler, D.: Instance-based learning algorithms. Mach. Learn. 6, 37–66 (1991)MATH
24.
go back to reference Holte, R.C.: Very simple classification rules perform well on most commonly used datasets. Mach. Learn. 11, 63–91 (1993)CrossRefMATH Holte, R.C.: Very simple classification rules perform well on most commonly used datasets. Mach. Learn. 11, 63–91 (1993)CrossRefMATH
25.
go back to reference Kohavi, R.: The power of decision tables. In: 8th European Conference on Machine Learning, 174–189 (1995) Kohavi, R.: The power of decision tables. In: 8th European Conference on Machine Learning, 174–189 (1995)
26.
go back to reference Berthold, M.R., Cebron, N., Dill, F., Gabriel, T.R., Kötter, T., Meinl, T., Ohl, P., Sieb, C., Thiel, K., Wiswedel, B.: KNIME: The Konstanz Information Miner, Studies in Classification, Data Analysis, and Knowledge Organization (GfKL 2007). Springer, Berlin (2007) Berthold, M.R., Cebron, N., Dill, F., Gabriel, T.R., Kötter, T., Meinl, T., Ohl, P., Sieb, C., Thiel, K., Wiswedel, B.: KNIME: The Konstanz Information Miner, Studies in Classification, Data Analysis, and Knowledge Organization (GfKL 2007). Springer, Berlin (2007)
27.
go back to reference Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. 11(1), 10–18 (2009) Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. 11(1), 10–18 (2009)
Metadata
Title
Liver Fibrosis Diagnosis Support System Using Machine Learning Methods
Authors
Tomasz Orczyk
Piotr Porwik
Copyright Year
2016
Publisher
Springer India
DOI
https://doi.org/10.1007/978-81-322-2650-5_8

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