A tree augmented naive Bayesian network experiment for breast cancer prediction
This work addresses breast cancer prediction for the aging population, but it is incremental as it applies an existing method to a specific demographic.
The study tackled breast cancer prediction in the aging population using DCIS grades, finding through a tree augmented naive Bayesian network that a biopsy threshold higher than 2% is recommended based on ten-fold cross-validation results.
In order to investigate the breast cancer prediction problem on the aging population with the grades of DCIS, we conduct a tree augmented naive Bayesian network experiment trained and tested on a large clinical dataset including consecutive diagnostic mammography examinations, consequent biopsy outcomes and related cancer registry records in the population of women across all ages. The aggregated results of our ten-fold cross validation method recommend a biopsy threshold higher than 2% for the aging population.