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OSDM: Optimized Shape Distribution Method

  • Ashkan Sami
  • Ryoichi Nagatomi
  • Makoto Takahashi
  • Takeshi Tokuyama
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4093)

Abstract

Comprehensibility is vital in results of medical data mining systems since doctors simply require it. Another important issue specific to some data sets, like Fitness, is their uniform distribution due to tile analysis that was performed on them. In this paper, we propose a novel data mining tool named OSDM (Optimized Shape Distribution Method) to give a comprehensive view of correlations of attributes in cases of uneven frequency distribution among different values of symptoms. We apply OSDM to explore the relationship of the Fitness 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, OSDM found several useful relationships.

Keywords

Benign Prostatic Hyperplasia Lower Urinary Tract International Prostate Symptom Score Fitness Test Subspace Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Cios, K.J., Moore, G.W.: Uniqueness of medical data mining. Artificial Intelligence in Medicine 26(1-2), 1–24 (2002)CrossRefGoogle Scholar
  2. 2.
    StatSoft, Inc. Electronic Statistics Textbook. StatSoft, Tulsa, OK (2006), WEB: http://www.statsoft.com/textbook/stathome.html
  3. 3.
    American Society of Consultant Pharmacologists, Health Trends (1998), http://www.ascp.com/public/pubs/tcp/1998/jun/trends.shtml
  4. 4.
    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)CrossRefGoogle Scholar
  5. 5.
    Agrawal, R., Gehrke, J., et al.: Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications. SIGMOD RECORD 27(2), 94–105 (1998)CrossRefGoogle Scholar
  6. 6.
    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)Google Scholar
  7. 7.
    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)CrossRefGoogle Scholar
  8. 8.
    Barry, M.J., Fowler Jr., F.J., et al.: The American Urological Association symptom index for benign prostatic hyperplasia. Journal of Urology 148(5), 1549–1557 (1992)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ashkan Sami
    • 1
  • Ryoichi Nagatomi
    • 2
  • Makoto Takahashi
    • 1
  • Takeshi Tokuyama
    • 3
  1. 1.Graduate School of EngineeringTohoku UniversityJapan
  2. 2.Department of Medicine and Science in Sports and Exercise, Graduate School of MedicineTohoku UniversityJapan
  3. 3.Graduate School of Information SciencesTohoku UniversityJapan

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