Predictive Modeling Versus Regression

Predictive Modeling Versus Regression

Patricia Cerrito (University of Louisville, USA)
Copyright: © 2010 |Pages: 43
DOI: 10.4018/978-1-60566-752-2.ch004

Abstract

Predictive modeling includes regression, both logistic and linear, depending upon the type of outcome variable. It can also include the generalized linear model. However, there are other types of models also available, including decision trees and artificial neural networks under the general term of predictive modeling. Predictive modeling includes nearest neighbor discriminant analysis, also known as memory based reasoning. These other models are nonparametric and do not require that you know the probability distribution of the underlying patient population. Therefore, they are much more flexible when used to examine patient outcomes. Because predictive modeling uses regression in addition to these other models, the end results will improve upon those found using just regression by itself.
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Background

Predictive modeling routinely makes use of a holdout sample to test the accuracy of the results. Figure 1 demonstrates predictive modeling. In SAS, there are two different regression models, three different neural network models, and two decision tree models. There is also a memory based reasoning model, otherwise known as nearest neighbor discriminant analysis. These models are discussed in detail in Cerrito (2007). It is not our intent here to provide an introductory text on neural networks; instead, we will demonstrate how they can be used effectively to investigate the outcome data.

Figure 1.

Predictive modeling of patient outcomes

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