Abstract
In our previous work, we had been proposed a new artificial immune system named as Attribute Weighted Artificial Immune System (AWAIS) to eliminate the negative effects of taking into account of all attributes in calculating Euclidean distance in shape-space representation which is used in many network-based Artificial Immune Systems (AISs). This system depends on the weighting attributes with respect to their importance degrees in class discrimination. These weights are then used in calculation of Euclidean distances. The performance analyses were conducted in the previous study by using machine learning benchmark datasets. In this study, the performance of AWAIS was investigated for real world problems. The used datasets were medical datasets consisting of Statlog Heart Disease and Pima Indian Diabetes datasets taken from University of California at Irvine (UCI) Machine Learning Repository. Classification accuracies for these datasets were obtained through using 10-fold cross validation method. AWAIS reached 82.59% classification accuracy for Statlog Heart Disease while it obtained a classification accuracy of 75.87% for Pima Indians Diabetes. These results are comparable with other classifiers and give promising performance to AWAIS for that kind of problems.
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
Preview
Unable to display preview. Download preview PDF.
References
Perelson, A.S., Oster, G.F.: Theoretical Studies of Clonal Selection: Minimal Anti-body Repertoire Size and Reliabilty of Self-Nonself Discrimination. Journal of Theoretical Biology 81, 645–670 (1979)
Mitchell, T.M.: Machine Learning, pp. 234–236. The McGraw-Hill Companies Press, New York (1997)
Hart, E.: Immunology as a Metaphor for Computational Information Processing: Fact or Fiction?, Doctor of Philosophy, Artificial Intelligence Applications Institute, Division of Informatics, University of Edinburgh, pp. 28–34 (2002)
Şahan, S., Kodaz, H., Güneş, S., Polat, K.: A new classifier based on attribute weighted artificial immune system. In: Aykanat, C., Dayar, T., Körpeoğlu, İ. (eds.) ISCIS 2004. LNCS, vol. 3280, pp. 11–20. Springer, Heidelberg (2004)
Blake, C.L., Merz, C.J.: UJI Reporsitory of Machine Learning Databases (1996), http://www.ics.uci.edu/~mlearn/MLReporsitory.htm
De Castro, L.N., Von Zuben, F.J.: Artificial Immune Systems: Part I-Basic Theory and Applications, Technical Report. TR-DCA 01/99, 3–14 (1999)
Abbas, A.K., Lichtman, A.H., Pober, J.S.: Cellular and Molecular Immunology, 4th edn. W.B. Saunders Company, Philadelphia (2000)
De Castro, L.N., Timmis, J.: Artificial Immune Systems: A New Computational Intelligence Approach. Springer, Heidelberg (2002)
Freitas, A., Timmis, J.: Revisiting the foundations of artificial immune systems: A problem-oriented perspective. In: Timmis, J., Bentley, P., Hart, E. (eds.) ICARIS 2003. LNCS, vol. 2787, pp. 229–241. Springer, Heidelberg (2003)
Emam, K.E., Benlarbi, S., Goel., N., Rai, S.N.: Comparing case-based reasoning classifiers for predicting high risk software components. The Journal of Systems and Software 55, 301–320 (2001)
Liao, T.W., Zhang, Z., Mount, C.R.: Similarity measures for retrieval in case-based reasoning systems. Applied Artificial Intelligence 12, 267–288 (1998)
Cherkassky, V., Mulier, F.: Learning From Data. John Wiley & Sons, Inc. (ed.), Chichester (1998)
Delen, D., Walker, G., Kadam, A.: Predicting breast cancer survivability: a comparison of three data mining methods. Artificial Intelligence in Medicine (September 2004) (in press)
http://www.phys.uni.torun.pl/kmk/projects/datasets.html#Sheart (last modified, 2002)
Jiang, Y., Zhou, Z.-H.: Editing training data for kNN classifiers with neural network ensemble. In: Yin, F.-L., Wang, J., Guo, C. (eds.) ISNN 2004. LNCS, vol. 3173, pp. 356–361. Springer, Heidelberg (2004)
Watkins, A.: AIRS: A Resource Limited Artificial Immune Classifier, Master Thesis, Mississippi State University (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Şahan, S., Polat, K., Kodaz, H., Güneş, S. (2005). The Medical Applications of Attribute Weighted Artificial Immune System (AWAIS): Diagnosis of Heart and Diabetes Diseases. In: Jacob, C., Pilat, M.L., Bentley, P.J., Timmis, J.I. (eds) Artificial Immune Systems. ICARIS 2005. Lecture Notes in Computer Science, vol 3627. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11536444_35
Download citation
DOI: https://doi.org/10.1007/11536444_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28175-7
Online ISBN: 978-3-540-31875-0
eBook Packages: Computer ScienceComputer Science (R0)