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Assessing a Bayesian risk prediction model in a high-risk breast cancer population.

Journal article published in 2007 by Jennifer Chun, Freya Schnabel ORCID, Omolola Ogunyemi
This paper is available in a repository.
This paper is available in a repository.

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Preprint: policy unknown
Question mark in circle
Postprint: policy unknown
Question mark in circle
Published version: policy unknown

Abstract

The purpose of this study was to utilize a Bayesian risk prediction model to predict the incidence of breast cancer in a high risk population. 10-fold cross-validation was performed using a Naïve Bayes classifier. The area under the ROC curve (AUC) was used to measure prediction accuracy. These results were then compared to the ROC curve (AUC) results of the Gail Model Risk Assessment Tool.