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A Comparison of Principal Component-Based and Multivariate Regression of Cardiac Disease

A Comparison of Principal Component-Based and Multivariate Regression of Cardiac Disease
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Author(s): Fox Underwood (University of Calgary, Canada)and Stefania Bertazzon (University of Calgary, Canada)
Copyright: 2013
Pages: 18
Source title: Geographic Information Analysis for Sustainable Development and Economic Planning: New Technologies
Source Author(s)/Editor(s): Giuseppe Borruso (University of Trieste, Italy), Stefania Bertazzon (University of Calgary, Canada), Andrea Favretto (University of Trieste, Italy), Beniamino Murgante (University of Basilicata, Italy)and Carmelo Maria Torre (Polytechnic of Bari, Italy)
DOI: 10.4018/978-1-4666-1924-1.ch003

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Abstract

Selecting factors suitable to use in a regression model is often a complicated process: the researcher strives to retain all theoretically important factors while avoiding high correlations among independent variables. This chapter models cardiac disease and compares the explanatory ability of component-based multivariate regression models, created through the use of principal component analysis (PCA), with that of direct variable-based, multivariate regression models. The variable-based demographic and socio-economic model contains education, sex, and 3 age factors; in contrast, the component-based model contains age as well as several modifiable risk factors: education, income, family, and housing factors. Moreover, the latter model also has statistically higher explanatory power. Components made through data reduction techniques may not always be interpretable, but, given closer examination of individual components, a component-based model becomes more interpretable. Further, all important factors will potentially be present in models. As such, component-based modelling can be a useful tool for research and public health planning. A key limitation of this work, to be addressed in future research, is the use of a variable (cardiac catheterisation procedures) that remains a crude proxy for cardiovascular disease. More effective analysis will be performed as data becomes available. Exploration into the relationship of factor and their spatial patterns will also be considered.

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