Abstract
Comprehensibility is driving force in medical data mining results since doctors utilize the outputs and give the final decision. Another important issue specific to some data sets, like physical activity, is their uniform distribution due to tile analysis that was performed on them In this paper, we propose a novel data mining tool named SDI (Shape Distribution Indicator) to give a comprehensive view of co-relations of attributes together with an index named ISDI to show the robustness of SDI outputs. We apply SDI to explore the relationship of the Physical Activity data and symptoms in medical test dataset for which popular data mining methods fail to give an appropriate output to help doctors decisions. In our experiment, SDI found several useful relationships.
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
Cios, K.J., Moore, G.W.: Uniqueness of medical data mining. Artificial Intelligence in Medicine 26(1-2), 1–24 (2002)
Sami, A.: Obstacles and Misunderstandings Facing Medical Data Mining. In: Li, X., Zaïane, O.R., Li, Z. (eds.) ADMA 2006. LNCS (LNAI), vol. 4093, pp. 856–863. Springer, Heidelberg (2006)
American Society of Consultant Pharmacologists, Health Trends (1998) (last visted 3/2006), http://www.ascp.com/public/pubs/tcp/1998/jun/trends.shtml
StatSoft, Inc: Electronic Statistics Textbook. StatSoft, Tulsa, OK (2006), http://www.statsoft.com/textbook/stathome.html
Okamura, K., Usami, T., Nagahama, K., Maruyama, S., Mizuta, E.: Quality of life assessment of urination in elderly Japanese men and women with some medical problems using International Prostate Symptom Score and King’s Health Questionnaire. European Urology 41(4), 411–419 (2002)
Agrawal, R., Gehrke, J., Gunopulos, D., Raghavan, P.: Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications. SIGMOD Record 27(2), 94–105 (1998)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proc. 2000 ACM SIGMOD Int. Conf. Management of Data (SIGMOD 2000), Dallas, TX, May 2000, pp. 1–12 (2000)
Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: Proc. of Fourth International Conference on Knowledge Discovery and Data Mining (1998)
Li, W., Han, J., Pei, J.: Cmar: Accurate and efficient classification based on multiple class-association rules. In: Proc. of First IEEE International Conference on Data Mining, pp. 369–376 (2001)
Dong, G., Zhang, X., Wong, L., Li, J.: Caep: Classification by aggregating emerging patterns. In: Arikawa, S., Furukawa, K. (eds.) DS 1999. LNCS (LNAI), vol. 1721, pp. 30–42. Springer, Heidelberg (1999)
Barry, M.J., Fowler Jr., F.J., O’Leary, M.P., Bruskewitz, R.C., Holtgrewe, H.L., Mebust, W.K., Cockett, A.T.: The American Urological Association symptom index for benign prostatic hyperplasia. Journal of Urology 148(5), 1549–1557 (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sami, A., Nagatomi, R., Takahashi, M., Tokuyama, T. (2006). SDI: Shape Distribution Indicator and Its Application to Find Interrelationships Between Physical Activity Tests and Other Medical Measures. In: Sattar, A., Kang, Bh. (eds) AI 2006: Advances in Artificial Intelligence. AI 2006. Lecture Notes in Computer Science(), vol 4304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941439_42
Download citation
DOI: https://doi.org/10.1007/11941439_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49787-5
Online ISBN: 978-3-540-49788-2
eBook Packages: Computer ScienceComputer Science (R0)