An association rule analysis framework for complex physiological and genetic data

Authors:
Jing He;Yanchun Zhang;Guangyan Huang;Yefei Xin;Xiaohui Liu;Hao Lan Zhang;Stanley Chiang;Hailun Zhang
Affiliations:
Victoria University, Melbourne, Australia;Victoria University, Melbourne, Australia;Victoria University, Melbourne, Australia;Victoria University, Melbourne, Australia;Brunel University, UK;NIT, Zhejiang University, China;TLC Medical PTY LTD, Australia;Step High Technology Co Ltd, China
Venue:
HIS'12 Proceedings of the First international conference on Health Information Science
Year:
2012

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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 ui (e.g. u1 numerical data streams, u2 images, u3 videos, u4 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.