ANALYSIS OF THE MEDICAL DATA STRUCTURE USING SELFORGANISING MAPS

Authors

  • O. V. Hodych
  • Yu. V. Nikolsky
  • V. V. Pasichnyk
  • Yu.Scherbyna M. Scherbyna

DOI:

https://doi.org/10.47839/ijc.6.3.461

Keywords:

Diagnostics, clustering, classification, artificial neural networks, data visualisation

Abstract

In this article the authors discuss several approaches to high dimensional data structure analysis using Self- Organising Maps. The describe approaches utilise graphical images for the purpose of data structure interpretation. The evaluation of the discussed techniques has been performed using the real medical data from cardiology. The research, results of which are outlined in this paper, is a continuation of the earlier work related to the analysis of the same medical data. It is envisaged that results obtained in this and earlier research work will form a foundation for creation of a robust technology to be used for automation of diagnostic tasks in medicine.

References

Czichosz P. Systemy uczace sie. – Wydawnictwa Naukowo-Techniczne, Warszawa, 2000.

J. Komorowski, Z. Pawlak, L. Polkowski and A. Skowron (1999). Rough sets: A tutorial. In: S.K. Pal and A. Skowron (eds.), Rough fuzzy hybridization: A new trend in decision-making, Springer-Verlag, Singapore, pp. 3-98.

Нікольський Ю.В., Пасічник В.В., Щербина Ю.М. Дискретна математика. – К.: Видавнича група BHV, 2007. – 368 с.

Jiawei Han, Micheline Kamber. Data Mining: Concepts and Techniques. – Morgan Kaufmann Publishers, 2001.

L.Kaufman, P.J.Rousseeuw. Finding Groups in Data: An Introduction to Cluster Analysis. – New York: John Wiley & Sons,1990.

Нікольський Ю.В. Застосування методів кластерного аналізу при побудові класифікуючих правил в задачі прийняття рішень // Вісник Національного університету “Львівська політехніка”, Інформаційні системи та мережі, 2003, № 489. – C.213-223.

Dunhan M.H. Data Mining Introductory and Advanced Topics. – Prentice Hall, 2003,

Годич О.В, Нікольський Ю.В., Пасічник В.В., Щербина Ю.М. Дослідження ефективності алгоритмів навчання мереж Кохонена. // Управляющие системы и машины, №2, 2006, с.63-80.

Matti Polla, Timo Honkela, Henrik Bruun. Analysis of Interdisciplinary Text Corpora, Proceedings of the 12th Finnish Artificial Intelligence Conference STeP 2006, Helsinki University of Technology, Finland, October 26-27, 2006, – pp. 17-22

Henrik Bruun, Sampsa Laine. Using the Self-Organizing Map for Measuring Interdisciplinary Research, Proceedings of the 12th Finnish Artificial Intelligence Conference STeP 2006, Helsinki University of Technology, Finland, October 26-27, 2006, – pp. 1-10.

Jorma Laaksonen, Ville Viitaniemi. Emergence of ontological relations from visual data with Self-Organizing Maps, Proceedings of the 12th Finnish Artificial Intelligence Conference STeP 2006, Helsinki University of Technology, Finland, October 26-27, 2006, – pp. 31-38.

M. Sirola, G. Lampi, J. Parviainen. SOM based decision support in failure manage-ment. International Journal of Computing, 4(3), 2005. – pp. 124-130.

Joseph A. Cruz, David S. Wishart. Applications of Machine Learning in Cancer Prediction and Prognosis, Cancer Informatics 2, 2006. – pp. 59-78.

Нікольський Ю.В., Щербина Ю.М., Якимечко Р.Я. Дерева прийняття рішень та їх застосування для прогнозування діагнозу у медицині // Вісн. Львів. ун-ту. Сер. прикл. мат. та інформ., 2003. – Вип. 4. – С. 191-211.

Годич О.В., Нікольський Ю.В., Щербина Ю.М. Застосування штучної нейронної мережі типу SOM для розв’язування задачі діагностування // Вісник Національного університету “Львівська політехніка”, 2002. – № 464. – С. 31-43.

A. Ultsch. Self-Organizing Neural Networks for Knowledge Akquisition. In Proc. of the 10th ECAI, Vienna, Austria, 1992, – pp. 208-210.

Si J., Lin S., Vuong M.-A. Dynamic Topology Representing Networks // Neural Networks, – 13 – 2000. – pp. 617-627.

Downloads

Published

2014-08-01

How to Cite

Hodych, O. V., Nikolsky, Y. V., Pasichnyk, V. V., & Scherbyna, Y. M. (2014). ANALYSIS OF THE MEDICAL DATA STRUCTURE USING SELFORGANISING MAPS. International Journal of Computing, 6(3), 124-136. https://doi.org/10.47839/ijc.6.3.461

Issue

2007, Volume 6, Issue 3

Section

Articles

License

International Journal of Computing is an open access journal. Authors who publish with this journal agree to the following terms:
•    Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
•    Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
•    Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.