Comparison of Bayes Classifiers for Breast Cancer Classification

Document Type: Research Articles

Authors

1 Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, India.

2 Department of Medicine, Professor, Chennai Medical College Hospital and Research Centre, Irungatur, Trichy, Tamilnadu, India.

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 prediction
and classification. Diseases like breast cancer can be classified based on the nature of the tumor. Finding an effective
algorithm for classification should help resolve the challenges present in analyzing large volume of data. The objective
with 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, TAN
produced the best performance regarding classification and accuracy. The results obtained provide clear evidence for
benefits of TAN usage in breast cancer classification. Applications of various machine learning algorithms could clearly
assist breast cancer control efforts for identification, prediction, prevention and health care planning.

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