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An Association Rule Analysis Framework for Complex Physiological and Genetic Data

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 7231)

Abstract

Physiological and genetic information has been critical to the successful diagnosis and prognosis of complex diseases. In this paper, we introduce a support-confidence-correlation framework to accurately discover truly meaningful and interesting association rules between complex physiological and genetic data for disease factor analysis, such as type II diabetes (T2DM). We propose a novel Multivariate and Multidimensional Association Rule mining system based on Change Detection (MMARCD). Given a complex data set u i (e.g. u 1 numerical data streams, u 2 images, u 3 videos, u 4 DNA/RNA sequences) observed at each time tick t, MMARCD incrementally finds correlations and hidden variables that summarise the key relationships across the entire system. Based upon MMARCD, we are able to construct a correlation network for human diseases.

Keywords

  • Association Rule
  • Retinal Image
  • Correlation Network
  • Fuzzy Association Rule
  • Physiological Observation

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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He, J. et al. (2012). An Association Rule Analysis Framework for Complex Physiological and Genetic Data. In: He, J., Liu, X., Krupinski, E.A., Xu, G. (eds) Health Information Science. HIS 2012. Lecture Notes in Computer Science, vol 7231. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29361-0_17

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  • DOI: https://doi.org/10.1007/978-3-642-29361-0_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29360-3

  • Online ISBN: 978-3-642-29361-0

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