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Developing a Discrimination Rule between Breast Cancer Patients and Controls Using Proteomics Mass Spectrometric Data: A Three-Step Approach

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  • Heidema A. Geert

    (Maastricht University; National Institute of Public Health and the Environment; Wageningen University and Research Centre)

  • Nagelkerke Nico

    (United Arab Emirates University)

Abstract

To discriminate between breast cancer patients and controls, we used a three-step approach to obtain our decision rule. First, we ranked the mass/charge values using random forests, because it generates importance indices that take possible interactions into account. We observed that the top ranked variables consisted of highly correlated contiguous mass/charge values, which were grouped in the second step into new variables. Finally, these newly created variables were used as predictors to find a suitable discrimination rule. In this last step, we compared three different methods, namely Classification and Regression Tree (CART), logistic regression and penalized logistic regression. Logistic regression and penalized logistic regression performed equally well and both had a higher classification accuracy than CART. The model obtained with penalized logistic regression was chosen as we hypothesized that this model would provide a better classification accuracy in the validation set. The solution had a good performance on the training set with a classification accuracy of 86.3%, and a sensitivity and specificity of 86.8% and 85.7%, respectively.

Suggested Citation

  • Heidema A. Geert & Nagelkerke Nico, 2008. "Developing a Discrimination Rule between Breast Cancer Patients and Controls Using Proteomics Mass Spectrometric Data: A Three-Step Approach," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(2), pages 1-11, February.
    • Handle: RePEc:bpj:sagmbi:v:7:y:2008:i:2:n:5
    DOI: 10.2202/1544-6115.1341
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    References listed on IDEAS

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    1. S. le Cessie & J. C. van Houwelingen, 1992. "Ridge Estimators in Logistic Regression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(1), pages 191-201, March.
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    Cited by:

    1. Gutiérrez, Luis & Gutiérrez-Peña, Eduardo & Mena, Ramsés H., 2014. "Bayesian nonparametric classification for spectroscopy data," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 56-68.

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