Los Angeles, CA
March 31, 2009 to April 2, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.420
Atherosclerosis results from chronic inflammatory processes involving a huge number of risk factor, such as lipid profile, glycated hemoglobin and oxidative stress. Signaling pathways are not enough to explain the holistic interactions. The combined use of principal component analysis and structural equation modeling is a good choice to investigate the causality between risk factors so as to complement the missing links and information in the published pathways. The result of demonstrates the combined use of these two methods in clustering of interacting risk factors and modeling their interactions. The clustered risk factors represent related features with causality. The most dominant cluster is the group of antioxidant, consisting of the essential substances responsible for eliminating harmful reactive oxygen species (ROS) and preventing atherosclerosis. The path model also uncovers the underlying interaction and balance between ascorbic acid and uric acid in human body.
Atherosclerosis, Principal Components Analysis, Structural Equation Modeling, Clustering, Interaction, Risk Factor
Lawrence W.C. Chan, "Structural Equation Modeling of Atherosclerotic Risk Factor Interactions", CSIE, 2009, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009, pp. 578-581, doi:10.1109/CSIE.2009.420