A Breast Cancer Detection Method Based on Bayesian Networks
Authors: 
Baodan Sun, Lu Dong, Ping Liu, Yi Ding, Sen ChenAuthors Info & Claims
Pages 933 - 937
Abstract
As an important model of machine learning, Bayesian networks are widely applied into medical diagnosis and achieve good performances in practical applications. Compared to black-box models, Bayesian networks can clearly show the dependent relations of random variables and the classification results are explainable. So, in this paper, we use Bayesian networks to evaluate the risk of breast cancer according to the Original Wisconsin Breast Cancer Database and also compare the impacts of different structure learning algorithms on the accuracy in the experiments. Experimental results show that Bayesian networks can effectively evaluate the risk of breast cancer and achieve good performances.
References
[1]
Estabragh, Z. S., M. M. R. Kashani, F. J. Moghaddam, S. Sari & K. S. Oskooyee. 2011. Bayesian Network Model for Diagnosis of Social Anxiety Disorder. In IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW), 639-640. Atlanta, GA.
[2]
Li, Y.-F., H.-Z. Huang, J. Mi, W. Peng & X. Han. 2022. Reliability analysis of multi-state systems with common cause failures based on Bayesian network and fuzzy probability. Annals of Operations Research, 311, 195-209.
[3]
Liu, Y., X. Ma, P. He, W. Qiao & H. Luo. 2022. Human factor risk modeling for shipyard operation by mapping fuzzy fault tree into bayesian network. International Journal of Environmental Research and Public Health, 19,1(January 1999), 297.
[4]
Beinlich, I. A., H. J. Suermondt, R. M. Chavez & G. F. Cooper. 1989. The ALARM Monitoring System: A Case Study with two Probabilistic Inference Techniques for Belief Networks. 247-256. Berlin, Heidelberg: Springer Berlin Heidelberg.
[5]
Ordovas, J. M., D. Rios-Insua, A. Santos-Lozano, A. Lucia, A. Torres, A. Kosgodagan & J. M. Camacho. 2023. A Bayesian network model for predicting cardiovascular risk. Computer Methods and Programs in Biomedicine, 231.
[6]
Curiac, D.-I., G. Vasile, O. Banias, C. Volosencu & A. Albu. 2009. Bayesian Network Model for Diagnosis of Psychiatric Diseases. In 31st International Conference on Information Technology Interfaces, 61-66. Cavtat, CROATIA.
[7]
Jones, A. M. & D. R. Jones. 2022. A Novel Bayesian General Medical Diagnostic Assistant Achieves Superior Accuracy With Sparse History A Performance Comparison of 7 Online Diagnostic Aids and Physicians. Frontiers in Artificial Intelligence, 5.
[8]
Chickering, D., Heckerman, D., & Meek, C. 2004. Large-sample learning of bayesian networks is np-hard. Journal of Machine Learning Research, 5, 1287–1330.
[9]
Russell SJ, Norvig P. 2009. Artificial Intelligence: A Modern Approach. Prentice Hall, 3rd edition.
[10]
samardinos, I., Brown, L. E., & Aliferis, C. F. 2006. The max-min hill-climbing Bayesian network structure learning algorithm. Machine Learning, 65, 1(March 2006), 31-78.
[11]
Gasse M, Aussem A, Elghazel H. 2014. A Hybrid Algorithm for Bayesian Network Structure Learning with Application to Multi-Label Learning. Expert Systems with Applications, 41, 15(November 2014), 6755–6772.
[12]
Wolberg,WIlliam. 1992. Breast Cancer Wisconsin (Original). UCI Machine Learning Repository. https://doi.org/10.24432/C5HP4Z
Index Terms
- A Breast Cancer Detection Method Based on Bayesian Networks
Recommendations
Computer-aided detection of breast cancer on mammograms
PSOWNN - Particle Swarm Optimized Wavelet Neural Network. DB - Database.Display Omitted We propose a CAD system for detecting breast cancer in mammograms.Swarm intelligence optimized wavelet neural network detects the cancers.We focus on optimized ...
Diagnosis of breast cancer using Bayesian networks: A case study
We evaluate the effectiveness of seven Bayesian network classifiers as potential tools for the diagnosis of breast cancer using two real-world databases containing fine-needle aspiration of the breast lesion cases collected by a single observer and ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
November 2023
1263 pages
ISBN:9798400708831
DOI:10.1145/3652628
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 23 May 2024
Check for updates
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
- Key R&D Program of Heilongjiang Province
- Fundamental Research Funds for the Central Universities
Conference
ICAICE 2023
ICAICE 2023: The 4th International Conference on Artificial Intelligence and Computer Engineering
November 17 - 19, 2023
Dalian, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 15Total Downloads
- Downloads (Last 12 months)15
- Downloads (Last 6 weeks)3
Reflects downloads up to 25 Nov 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format