title: Nomogram of Naive Bayesian Model for Recurrence Prediction of Breast Cancer.
abstract: OBJECTIVE
  Breast cancer has a high rate of recurrence, resulting in the need for aggressive treatment and close follow-up. However, previously established classification guidelines, based on expert panels or regression models, are controversial. Prediction models based on machine learning show excellent performance, but they are not widely used because they cannot explain their decisions and cannot be presented on paper in the way that knowledge is customarily represented in the clinical world. The principal objective of this study was to develop a nomogram based on a naïve Bayesian model for the prediction of breast cancer recurrence within 5 years after breast cancer surgery.
  
  
  METHODS
  The nomogram can provide a visual explanation of the predicted probabilities on a sheet of paper. We used a data set from a Korean tertiary teaching hospital of 679 patients who had undergone breast cancer surgery between 1994 and 2002. Seven prognostic factors were selected as independent variables for the model.
  
  
  RESULTS
  The accuracy was 80%, and the area under the receiver operating characteristics curve (AUC) of the model was 0.81.
  
  
  CONCLUSIONS
  The nomogram can be easily used in daily practice to aid physicians and patients in making appropriate treatment decisions after breast cancer surgery.
authors.affiliation: Department of Public Health and Medical Administration, Dongyang University, Yeongju, Korea.
authors.forename: Woojae
authors.initials: W
authors.lastname: Kim
authors.affiliation: Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea.; Breast Cancer Center, Ulsan City Hospital, Ulsan, Korea.
authors.forename: Ku Sang
authors.initials: KS
authors.lastname: Kim
authors.affiliation: Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea.
authors.forename: Rae Woong
authors.initials: RW
authors.lastname: Park
keywords: Breast Neoplasms; Data Mining; Decision Support Techniques; Neural Networks; Support Vector Machine; Survival Analysis