title: Assessing a Bayesian risk prediction model in a high-risk breast cancer population.
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.
authors.affiliation: Department of Surgery, Comprehensive Breast Center, Women At Risk, New Yourk Presbyterian Hospital, Columbea University Medical Center, New Yourk, NY, USA.
authors.forename: Jennifer
authors.initials: J
authors.lastname: Chun
authors.forename: Freya
authors.initials: F
authors.lastname: Schnabel
authors.forename: Omolola
authors.initials: O
authors.lastname: Ogunyemi
mesh_terms: D019540:Area Under Curve; D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000453:epidemiology*; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D015994:Incidence; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D012372:ROC Curve; D012306:Risk