A tree augmented naive Bayesian network experiment for breast cancer prediction

2015/06/18 by Ping Ren, Ren, Ping
Biochemistry, Genetics and Molecular Biology · Computer Science · #AI in cancer detection #Biomedical Text Mining and Ontologies #FOS: Biological sciences #FOS: Computer and information sciences #Gene expression and cancer classification #Machine Learning (stat.ML) #Quantitative Methods (q-bio.QM)

paper · pdf · doi:10.48550/arxiv.1506.05776

openalex publication_date 2015/06/18 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Overdiagnosis in Cancer
• Unachievable Region in Precision-Recall Space and Its Effect on Empirical Evaluation

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