@article { author = {Banu A, Bazila and Thirumalaikolundusubramanian, Ponniah}, title = {Comparison of Bayes Classifiers for Breast Cancer Classification}, journal = {Asian Pacific Journal of Cancer Prevention}, volume = {19}, number = {10}, pages = {2917-2920}, year = {2018}, publisher = {West Asia Organization for Cancer Prevention (WAOCP), APOCP's West Asia Chapter.}, issn = {1513-7368}, eissn = {2476-762X}, doi = {10.22034/APJCP.2018.19.10.2917}, abstract = {Data analytics play vital roles in diagnosis and treatment in the health care sector. To enable practitioner decisionmaking,huge volumes of data should be processed with machine learning techniques to produce tools for predictionand classification. Diseases like breast cancer can be classified based on the nature of the tumor. Finding an effectivealgorithm for classification should help resolve the challenges present in analyzing large volume of data. The objectivewith this paper was to present a report on the performance of Bayes classifiers like Tree Augmented Naive Bayes(TAN), Boosted Augmented Naive Bayes (BAN) and Bayes Belief Network (BBN). Among the three approaches, TANproduced the best performance regarding classification and accuracy. The results obtained provide clear evidence forbenefits of TAN usage in breast cancer classification. Applications of various machine learning algorithms could clearlyassist breast cancer control efforts for identification, prediction, prevention and health care planning.}, keywords = {Tree Augmented Naive Bayes (TAN),Boosted Augmented Naive Bayes (BAN),Bayes Network (BN),Gradient Boosting (GB)}, url = {https://journal.waocp.org/article_69123.html}, eprint = {https://journal.waocp.org/article_69123_998d1e424189e63fbb8a29240cb03ebd.pdf} }