Abstract
One of the most critical concerns in different fields, particularly in the medical domain, is the time taken to arrive at a decision. In Egypt, Liver fibrosis is one of the medical conditions affecting a large number of people across different genders and ages. Thus, early detection of fibrosis in hepatic patients is considered a challenging area for many physicians. The using of machine learning techniques and strategic information systems highly aid in solving the problem of classification of data and knowledge acquisition. In this study linear and even multi-dimensional analysis combined with K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classifiers are applied. Results yield to an accuracy of 98% to cubic SVM classifier compared to 75% accuracy to medium KNN.
All the hepatic fibrosis patients involved in the study were diagnosed at department of Biochemistry and Molecular Biology of Kasr Alainy Hospital of Cairo University. For all participants in this study, written informed consent was obtained as delineated by the protocol which was approved by the Ethical Committee of Cairo University.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Gebo, K., Herlong, H., Torbenson, M., et al.: Role of liver biopsy in the management of chronic hepatitis C: a systematic review. Hepatology 36, 161–172 (2002)
Cadranel, J., Rufat, P., Degos, F.: Practices of liver biopsy in France: results of a prospective nationwide survey. Hepatology 32, 477–481 (2000)
Sakugawa, H., Nakayoshi, T., Kobashigawa, K., et al.: Clinical usefulness of biochemical markers of liver fibrosis in patients with non alcoholic fatty liver disease. World J. Gastroenterol. 11, 255–259 (2005)
Halfon, P., Bourlire, M., Pnaranda, G., et al.: Accuracy of hyaluronic acid level for predicting liver fibrosis stages in patients with hepatitis C virus. Comp. Hepatol. 4, 1–7 (2005)
Ayeldeen, H., Shaker, O., Hegazy, O., Hassanien, A.E.: Distance similarity as a CBR technique for early detection of breast cancer: an Egyptian case study. Inf. Syst. Des. Intell. Appl. 340, 449–456 (2015)
Ayeldeen, H., Hassanien, A.E., Fahmy, A.A.: Lexical similarity using fuzzy Euclidean distance. In: 2nd International Conference of Engineering and Technology, IEEE CPS Proceedings, Cairo, Egypt, pp. 1–6 (2014)
Daum III, H.: A course in machine learning. Publisher, ciml.info, pp. 5–73 (2012)
Shalev-Shwartz, S., Ben-David, S.: Understanding Machine Learning from Theory to Algorithms, 1st edn. Cambridge University Press, Cambridge (2014)
Wu, Q., Zhou, D.: Analysis of support vector machine classification. Int. J. Comput. Anal. Appl. 8, 99–119 (2006)
Cortes, C., Vapnik, V.N.: Support vector networks. Mach. Learn. 20, 273–297 (1995)
Kibriya, A., Frank, E.: An empirical comparison of exact nearest neighbour algorithms. In: Proceedings of the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2007, vol. 4702, pp. 140–151. Springer (2007)
Coleman, R.M., Ralston, M.D., Szafran, A., Beaulieu, D.M.: Multidimensional analysis: a management tool for monitoring HIPAA compliance and departmental performance. J. Digit. Imaging 17(3), 196–204 (2004)
Ayeldeen, H., Shaker, O., Ayeldeen, G., Anwar, K.M.: Prediction of liver fibrosis stages by machine learning model: a decision tree approach. In: Third World Conference on Complex Systems (WCCS15). IEEE (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Bakry, U., Ayeldeen, H., Ayeldeen, G., Shaker, O. (2018). Classification of Liver Fibrosis Patients by Multi-dimensional Analysis and SVM Classifier: An Egyptian Case Study. In: Bi, Y., Kapoor, S., Bhatia, R. (eds) Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016. IntelliSys 2016. Lecture Notes in Networks and Systems, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-319-56994-9_75
Download citation
DOI: https://doi.org/10.1007/978-3-319-56994-9_75
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-56993-2
Online ISBN: 978-3-319-56994-9
eBook Packages: EngineeringEngineering (R0)