Principal Component Analysis as an Integral Part of Data Mining in Health Informatics

Linear and logistic regression are well-known data mining techniques, however, their ability to deal with interdependent variables is limited. Principal component analysis (PCA) is a prevalent data reduction tool that both transforms the data orthogonally and reduces its dimensionality. In this paper we explore an adaptive hybrid approach where PCA can be used in conjunction with logistic regression to yield models which have both a better fit and a reduced set of factors than those produced by just the regression analysis. We will use example dataset from HealthData.gov to demonstrate the simplicity, applicability and usability of our approach. keywords: Principal component analysis, Regression analysis, healthcare analytics, big data analytics