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.
Keywords
- Physical Activity
- Benign Prostatic Hyperplasia
- Association Rule
- International Prostate Symptom Score
- Frequent Itemsets
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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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
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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
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