Artificial immune systems applied to clinical diagnosis of breast cancer samples

Authors

DOI:

https://doi.org/10.33448/rsd-v11i5.21941

Keywords:

Breast cancer diagnosis; Artificial immune systems; Negative selection algorithm.

Abstract

This work employments a manufactured resistant framework connected for diagnosing breast cancer tests. Taking as premise an immunological prepare, the Negative Selection Algorithm was utilized to segregate the tests, achieving a classification for generous or harmful cases. The most application of the strategy is to help experts within the breast cancer demonstrative prepare, in this manner giving decision-making agility, efficient treatment arranging, unwavering quality and the vital mediation to spare lives. To assess this strategy, the Wisconsin Breast Cancer Determination database was utilized. This is often a real breast cancer database. The comes about gotten utilizing the strategy, when compared with the specialized writing, appear precision, strength and proficiency within the breast cancer demonstrative handle.

References

Bennett, K. P., & Mangasarian, O. L. (1992). Robust Linear Programming Discrimination of Two Linearly Inseparable Sets, Optimization Methods and Software, Gordon & Breach Science Publishers, 23-34.

Bradley, D. W., & Tyrrell, A.M. (2002). Immunotronics - Novel Finite-State-Machine Architectures with Built-In Self-Test Using Self-Nonself Differentiation. IEEE Trans. on Evolutionary Computation. 6 (3), 227-238.

Camastra, F. (2006). Kernel Methods for Clustering. Lecture Notes in Computer Science. 3931, 1–9.

Castro, L. N. (2001). Immune engineering: development and application of computational tools inspired by artificial immune systems. PhD. Thesis. UNICAMP. Campinas, São Paulo, Brazil. (In Portuguese).

Castro, L. N. & Timmis, J. (2002). Artificial Immune Systems: A New Computational Intelligence Approach, Springer.

Dasgupta, D. (1998). Artificial Immune Systems and Their Applications, Springer, New York, USA.

Dasgupta, D. (2006). Advances in Artificial Immune Systems. IEEE Computational Intelligence Magazine, 40-49.

Forrest, S., Hofmeyr, S. A., & Somayaji, A. (1997). Computer Immunology. Communications of the AC. 88-96.

Forrest, S., Perelson, A., Allen, L., & Cherukuri, R. (1994). Self-Nonself Discrimination in a Computer, Proc. of IEEE Symposium on Research in Security and Privacy. 202-212.

Hamdi, R. El., Njah, M., & Chtourou, M. (2010). An Evolutionary Neuro-Fuzzy Approach to Breast Cancer Diagnosis. IEEE International Conference on Systems, Man and Cybernetics, 142-146.

INCA – National Institute of Cancer (Brazil). Available at: http://www.inca.gov.br.

Jung, J-S. R. (1993) ANFIS: Adaptive Network-Based Fuzzy Inference System. IEEE Trans. on Systems, Man, and Cybernetics. 23 (3), 665-685.

Karabatak, M., Ince, M. C., & Avci, E. (2008). An Expert System for Diagnosis Breast Cancer Based on Principal Component Analyses Method. IEEE Proceedings on Communication and Applications Conference, 1-4.

Lima, F. P. A., Lotufo, A. D. P., & Minussi, C. R. (2013). Artificial Immune Systems Applied to Voltage Disturbance Diagnosis in Distribution Electrical Systems, PowerTech-2013, Grenoble, France, 1-6.

Mangasarian O. L., Setiono, R., & Wolberg, W. H. (1990). Pattern Recognition Via Linear Programming: Theory and Application to Medical Diagnosis. Large-scale Numerical Optimization, 22-30.

Manikantan, K., Sayed, S.I., Syrigos, K.N., Rhys-Evans, P., Nutting, C.M., Harrington, K.J., & Kazi, R. Challenges for The Future Modifications of The TNM Staging System for Head and Neck Cancer: Case for a New Computational Model. Cancer Treatment Reviews, 35 (7), 639-644.

MATLAB 7.8 version, Mathworks Company.

Meesad, P. & Yen, G. G. (2003). Combined Numerical and Linguistic Knowledge Representation and Its Application to Medical Diagnosis. IEEE Trans. on Systems, Man, and Cybernetics -Part A: Systems and Humans. 33 (2), 206-222.

Naghibi, S. S., Teshnehlab, M. & Shoorehdeli, M. A. “Breast Cancer Detection by Using Hierarchical Fuzzy Neural System with EKF Trainer”, IEEE Proceedings of the 17th Iranian Conference of Biomedical Engineering - ICBME2010, November-2010, pp. 1-4.

OMS – World Health Organization. http://www.who.int/en/

Pena-Reyes, C. A., & Sipper, M. (1999). Designing Breast Cancer Diagnostic System Via Hybrid Fuzzy-Genetic Methodology. IEEE International Fuzzy Systems Conference Proceeding. 135-139.

Polat, K., Sahan, S., Kodaz, H. E., & Gunes, S. (2007). Breast Cancer and Liver Disorders Classifications Using Artificial Immune Recognition System (AIRS) With Performance Evaluation by Fuzzy Resource Allocation Mechanism, Expert Systems with Applications, 32 (1), 172–183.

Song, H-J, Lee, S-G., & Park, G-T. (2005). A Methodology of Computer Aided Diagnostic System on Breast Cancer. Proceedings of the 2005 IEEE Conference on Control Applications Toronto, 831-836.

Wang, J-S., & Lee, C.S.G. Self-Adaptive Neuro-Fuzzy Inference Systems for Classification Applications. IEEE Transactions on Fuzzy Systems, 10 (6), 790-802.

Wang, J.-Y. (2005). Data Mining Analysis (Breast-Cancer Data). http://www.csie.ntu.edu.tw/ p88012/AI-final.pdf.

WBCD – Wisconsin Breast Cancer Data – UCI Machine Learning Repository. www.arquives.ics.uci.edu/ml/

Wolberg, W. H., & Mangasarian, O. L. (1990). Multisurface Method of Pattern Separation for Medical Diagnosis Applied to Breast Cytology. Proceedings of the National Academy of Sciences of USA, 87 (23), 9193-9196.

Zhao, W. & Davis, C. E. (2011). A Modified Artificial Immune System Based Pattern Recognition Approach – An Application to Clinical Diagnostics. Artificial Intelligence in Medicine. 52 (1), 1-9.

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Published

29/03/2022

How to Cite

SOUZA, S. S. F. de .; CHAVARETTE, F. R. .; LIMA, F. P. dos A. . Artificial immune systems applied to clinical diagnosis of breast cancer samples. Research, Society and Development, [S. l.], v. 11, n. 5, p. e3711521941, 2022. DOI: 10.33448/rsd-v11i5.21941. Disponível em: https://www.rsdjournal.org/index.php/rsd/article/view/21941. Acesso em: 20 apr. 2024.

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Section

Exact and Earth Sciences