Nomogram of Naive Bayesian Model for Recurrence Prediction of Breast Cancer

Abstract

OBJECTIVES: 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 naive 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.