Abstract
The objective of this study was to design, to implement and to validate a framework for the development of decision system support based on fuzzy set theory using clusters and dynamic tables. To validate the proposed framework, a fuzzy inference system was developed with the aim to classify breast cancer and compared with other related works (Literature). The fuzzy Inference System has three input variables. The results show that the Kappa Statistics and accuracy were 0.9683 and 98.6%, respectively for the output variable for the Fuzzy Inference System – FIS, showing a better accuracy than some literature results. The proposed framework may provide an effective means to draw a pattern to the development of fuzzy systems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
American Cancer Society: Cancer Facts & Figures 2018, p. 76. American Cancer Society Inc., Atlanta (2018)
Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2018. CA Canc. J. Clinic. 68(1), 7–30 (2018)
Onan, A.: A fuzzy-rough nearest neighbor classifier combined with consistency-based subset evaluation and instance selection for automated diagnosis of breast cancer. Exp. Syst. Appl. 42(20), 6844–6852 (2015)
Hayat, M.A.: Breast cancer: an introduction. In: Hayat, M.A. (ed.) Methods of Cancer Diagnosis, Therapy and Prognosis. Methods of Cancer Diagnosis, Therapy and Prognosis, vol. 1. Springer, Dordrecht (2008)
Yazdanbakhsh, O., Dick, S.: A systematic review of complex fuzzy sets and logic. Fuzzy Sets Syst. 338, 1–22 (2018)
Arslan, E., et al.: Rule based fuzzy logic approach for classification of fibromyalgia syndrome. Australas. Phys. Eng. Sci. Med. 39(2), 501–515 (2016)
Nilashi, M., et al.: A knowledge-based system for breast cancer classification using fuzzy logic method. Telemat. Inform. 34(4), 133–144 (2017)
Gayathri, B.M., Sumathi, C.P.: Mamdani fuzzy inference system for breast cancer risk detection. In: IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE (2015)
Bache, K., Lichman, M.: UCI machine learning repository. University of California, School of Information and Computer Science, Irvine (2013)
Mangasarian, O.L.: Cancer diagnosis via linear programming. SIAM News 23(5), 1–18 (1990)
Aghabozorgi, S., Teh, Y.W.: Stock market co-movement assessment using a three-phase clustering method. Expert Syst. Appl. 41(4, Part 1), 1301–1314 (2014)
IBM Corp.: Released 2015. IBM SPSS Statistics for Windows. IBM Corp., Armonk (2015)
Caliński, T., Harabasz, J.: A dendrite method for cluster analysis. Commun. Stat. Theor. Methods 3(1), 1–27 (1974)
Leach, A.R., Gillet, V.J.: An Introduction to Chemoinformatics. Springer, Dordrecht (2007)
Malhat, M.G., El-Sisi, A.B.: Parallel ward clustering for chemical compounds using OpenCL. In: Tenth International Conference on Computer Engineering & Systems (ICCES) (2015)
Hernández-Julio, Y.F., et al.: Fuzzy system to predict physiological responses of Holstein cows in southeastern Brazil. Rev. Col. Cienc. Pecu. 28(1), 42–53 (2015)
Tanaka, K.: An Introduction to Fuzzy Logic for Practical Applications, 1st edn. Springer, New York (1996)
Sivanandam, S., Sumathi, S., Deepa, S.: Introduction to Fuzzy Logic Using MATLAB, vol. 1. Springer, Heidelberg (2007)
The MathWorks Inc.: Design and Simulate Fuzzy Logic Systems. The MathWorks Inc. (2017)
Pota, M., Esposito, M., De Pietro, G.: Designing rule-based fuzzy systems for classification in medicine. Knowl. Based Syst. 124, 105–132 (2017)
Ali, F., et al.: Type-2 fuzzy ontology–aided recommendation systems for IoT–based healthcare. Comp. Commun. 119, 138–155 (2018)
Nguyen, T., et al.: Medical data classification using interval type-2 fuzzy logic system and wavelets. Appl. Soft Comput. 30, 812–822 (2015)
Acknowledgments
The first author expresses his deep thanks to the Administrative Department of Science, Technology, and Innovation – COLCIENCIAS of Colombia and the Universidad del Norte for the Doctoral scholarship. Also expresses their deep thanks to the Universidad del Sinú Elías Bechara Zainúm for the scholar and financial support.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Hernández-Julio, Y.F., Hernández, H.M., Guzmán, J.D.C., Nieto-Bernal, W., Díaz, R.R.G., Ferraz, P.P. (2019). Fuzzy Knowledge Discovery and Decision-Making Through Clustering and Dynamic Tables: Application in Medicine. In: Rocha, Á., Ferrás, C., Paredes, M. (eds) Information Technology and Systems. ICITS 2019. Advances in Intelligent Systems and Computing, vol 918. Springer, Cham. https://doi.org/10.1007/978-3-030-11890-7_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-11890-7_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-11889-1
Online ISBN: 978-3-030-11890-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)