ArticleAsian Pacific journal of cancer prevention : APJCP2018
Comparison of Bayes Classifiers for Breast Cancer Classification
Article in Asian Pacific journal of cancer prevention : APJCP, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Classification of NSCLC subtypes using lung microbiome from resected tissue based on machine learning methods.NPJ systems biology and applications · 2025Article
- Breast cancer prediction using different machine learning methods applying multi factors.Journal of cancer research and clinical oncology · 2023Article
- Identifying individualized risk subpathways reveals pan-cancer molecular classification based on multi-omics data.Computational and structural biotechnology journal · 2022Article
- Early lung cancer diagnostic biomarker discovery by machine learning methods.Translational oncology · 2021Article
- Machine learning techniques for mortality prediction in critical traumatic patients: anatomic and physiologic variables from the RETRAUCI study.BMC medical research methodology · 2020Article
- RNA-Seq-Based Breast Cancer Subtypes Classification Using Machine Learning Approaches.Computational intelligence and neuroscience · 2020Article
- Analysis of Decision Tree and K-Nearest Neighbor Algorithm in the Classification of Breast Cancer.Asian Pacific journal of cancer prevention : APJCP · 2019Article
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2 authors.
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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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