Discovering knowledge about causes of diabetes using an approach based on rough sets

• Autorzy: Gościewska K.
• Języki publikacji: EN

Abstrakty

EN

Diabetes is called a disease of civilization. It occurs when the blood glucose level is too high and insulin level is too low to transport glucose into cells to produce energy. If left untreated, the diabetes can lead to long-term complications causing severe damages to the human body. Therefore, it is desirable to become aware of the causes that can contribute to the development of this illness. In this paper, the rough sets approach is applied for knowledge discovery. The main goal is to obtain simple rules from the given diabetes dataset that would reveal patterns hidden in it.

Słowa kluczowe

EN

diabetes; rough sets; rule induction from numerical data; data mining; knowledge; discovery; UCI dataset

• Wydawca: Komisja Informatyki Polskiej Akademii Nauk, Oddział w Gdańsku
• Czasopismo: Journal of Theoretical and Applied Computer Science
• Rocznik: 2014
• Tom: Vol. 8, nr 3
• Strony: 23--30
• Opis fizyczny: Bibliogr. 12 poz., tab.

Twórcy

autor

Gościewska K.

• Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, Szczecin, Poland

Bibliografia

• [1] Ohrn, A., Rowland, T.: Rough Sets: A Knowledge Discovery Technique for Multifactorial Medical Outcomes, American Journal of Physical Medicine & Rehabilitation 79(1), 100-108, 2000.
• [2] Levrac, N.: Machine Learning for Data mining in Medicine. In: Horn W. et al. (Eds.): AIMDM’99, LNAI 1620, pp. 47-62. Springer Verlag Heidelberg, 1999.
• [3] Pawlak, Z.: Rough sets, International Journal of Computer and Information Sciences, 11, 341-356, 1982.
• [4] Pawlak, Z.: Rough Sets Theory For Intelligent Industrial Applications, Proceedings of the Second International Conference on Intelligent Processing and Manufacturing of Materials, vol. 1, pp. 37-44, 1999.
• [5] Pawlak, Z.: Rough set theory and its applications, Journal of Telecommunications and Information Technology 3, 7-10, 2002.
• [6] Settouti, N., Chikh, M. A., Saidi, M.: Generating fuzzy rules for constructing interpretable classifier of diabetes disease, Australasian Physical and Engineering Sciences in Medicine 35(3), 257-270, 2012.
• [7] Stepaniuk J.: Rough Set Data Mining of Diabetes Data. In: Raś Z.W., Skowron A. (Eds.) ISMIS’99, LNCS 1609, pp. 457-465. Springer Berlin Heidelberg, 1999.
• [8] Hassanien, A. E., Abdelhafez, M. E., Own, H. S.: Rough Sets Data Analysis in Knowledge Discovery: A Case of Kuwaiti Diabetic Childer Patients, Advances in Fuzzy Systems 8. Hindawi Publishing Corporation, 2008.
• [9] Matthews, D. et. al.: Diabetes. The facts. Oxford University Press, 2008.
• [10] http://archive.ics.uci.edu/ml/datasets/Pima+Indians+Diabetes [access: June 2013]
• [11] Pawlak, Z.: Rough sets and intelligent data analysis, Information Sciences 174, 1-12, 2002.
• [12] Nakayama, H., Hattori, Y., Ishii, R.: Rule Extraction based on Rough Sets Theory and its Application to Medical Data Analysis, 1999 IEEE Internation Conference on Systems, Man, and Cybernetics, vol. 5, pp. 924-929, 1999.