Abstract
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.
| Original language | American English |
|---|---|
| Journal | Proceedings of the 31st International Conference on Computers and Their Applications (2016, Las Vegas, NV) |
| State | Published - Apr 1 2016 |
Keywords
- Big data
- Big data analytics
- Data analytics
- Data handling
- Data mining
- Data reduction
- Health informatics
- Healthcare analytics
- Hybrid approach
- Integral part
- Logistic regressions
- Principal component analysis
- Regression analysis
- Yield models
Disciplines
- Computer Sciences
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