Using artificial immune and case-based reasoning methods in classification of treatment effectiveness

• Autorzy: Badura D. , Ferdynus D.
• Języki publikacji: EN

Abstrakty

EN

The article concerns the analysis of classification of medical data by use of selected method of artificial intelligence: case-based reasoning. The subject of the research is the assessment of effective treatment, being one of the most important medical problems. The basis work of the assessment system should be one of the classification methods. The aim of the attempted research is to study which of the enumerated method will be able to group data containing incomplete information in the best way. The classified data are descended from the patients with nephroblastoma and patients with backbone pain. The final aim of the research is to work out the functioning method of the learning system, assisting the doctor with making a decision during working out on patient's treatment therapy, and making analyses of the treatment effectiveness. On the basis of the medical tests, the system will classify the data assigning them to the appropriate therapy groups. Moreover, in the system will be used artificial immunology as the method of generalizing or extrapolating of the gathering and considering so far cases.

Słowa kluczowe

EN

case-based reasoning; artificial immune system; fuzzy neural nets

PL

wnioskowanie bazujące na przykładach; system immunologiczny sztuczny; sieci neuronowe rozmyte

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2007
• Tom: Vol. 11
• Strony: 221--226
• Opis fizyczny: Bibliogr. 10 poz., rys.

Twórcy

autor

Badura D.

• Department of Design and Diagnostic of Systems, Institute of Computer Science, Silesian University of Katowice, Sosnowiec ul. Będzinska 39, tel./fax: [phone]

autor

Ferdynus D.

Bibliografia

• [1] GAMA J., Combining classification algorithms, PhD Thesis, University of Porto, 1999.
• [2] AAMODT A.; PLAZA E.; Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches; Agnar Aamodt; Enric Plaza; AI Communications, Vol. 7 Nr. 1, March 1994, pp 39-59.
• [3] SCHMIDT R., POLLWEIN B., GIERL L. Case-Based Techniques in an Antibiotics Therapy Adviser;.
• [4] HUNT J.E., COOKE D.E.: “Learning using an artificial immune system”; Journal of Network and Computer Applications (1996) 19, pp.189–212.
• [5] BADURA D. FERDYNUS D. „Grupowanie obiektów za pomocą Fuzzy Neural Nets na przykładzie pacjentów chorych na nerczaka zarodkowego.” Proccedings of the XXVI th International Autumm Colloquium, Advanced Simulation of Systems, ASIS, Hostyn, Czech. Republic, 22 – 24. 09.2004, pp 207-211.
• [6] BADURA D, FERDYNUS D, DYSZKIEWICZ A., Leśnik M. „Klasyfikacja obiektów za pomocą sieci neuronowych wykorzystujących logikę rozmytą oraz wnioskowania opartego na bazie przypadków.” XIV Krajowa Konferencja Biocybernetyki i Informatyki Biomedycznej, 21-23.09.2005, Częstochowa, pp 925-928.
• [7] KRAVIS S., IRRGANG R.: A Case Based System for Oil and Gas Well Design with Risk Assessment; Applied Intelligence 23, pp. 39–53, 2005; 2005 Springer Science + Business Media.
• [8] YAGER R.R.: Soft Aggregation Methods in Case Based Reasoning;Applied Intelligence 21, pp. 277–288, 2004; 2004 Kluwer Academic Publishers.
• [9] ZHANG ZHONG, YANG QIANG: Feature Weight Maintenance in Case Bases Using Introspective Learning; Journal of Intelligent Information Systems, 16, pp. 95–116, 2001 2001 Kluwer Academic Publishers.
• [10] ONIŚKO A., BOBROWSKI L., DRUZDZEL M.J., WASYLUK H.: HEPAR i HEPAR II – komputerowe systemy wspomagające diagnozowanie chorób wątroby*; Proceedings of the 12th Conference on Biocybernetics and Biomedical Engineering, Warsaw, Poland, 28-30 November 2001.