IMR Press / FBE / Volume 2 / Issue 3 / DOI: 10.2741/E146

Frontiers in Bioscience-Elite (FBE) is published by IMR Press from Volume 13 Issue 2 (2021). Previous articles were published by another publisher on a subscription basis, and they are hosted by IMR Press on imrpress.com as a courtesy and upon agreement with Frontiers in Bioscience.

Open Access Article
Comparison of the predictive qualities of three prognostic models of colorectal cancer
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1 SAS Institute, Cary, NC
2 Department of Information Systems, Statistics, and Management, University of Alabama at Tuscaloosa, AL
3 Exponent Health Sciences, Wood Dale, IL
4 Division of Preventive Medicine, Department of Pathology, Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL 35294
5 Department of Pathology, Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL 35294
6 Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL 35294
Academic Editor:Rakesh Srivastava
Front. Biosci. (Elite Ed) 2010, 2(3), 849–856; https://doi.org/10.2741/E146
Published: 1 June 2010
(This article belongs to the Special Issue Cancer chemoprevention - 2)
Abstract

Most discoveries of cancer biomarkers involve construction of a single model to determine predictions of survival.. 'Data-mining' techniques, such as artificial neural networks (ANNs), perform better than traditional methods, such as logistic regression. In this study, the quality of multiple predictive models built on a molecular data set for colorectal cancer (CRC) was evaluated. Predictive models (logistic regressions, ANNs, and decision trees) were compared, and the effect of techniques for variable selection on the predictive quality of these models was investigated. The Kolmogorov-Smirnoff (KS) statistic was used to compare the models. Overall, the logistic regression and ANN methods outperformed use of a decision tree. In some instances (e.g., for a model that included 'all variables without tumor stage' and use of a decision tree for variable selection), the ANN marginally outperformed logistic regression, although the difference between the accuracy of the KS statistic was minimal (0.80 versus 0.82). Regardless of the variable(s) and the methods for variable selection, all three predictive models identified survivors and non-survivors with the same level of statistical accuracy.

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