Abstract
Medical industry, though various researches over decades, has figured out breast cancer to be one of the most common diseases in women. Studies have shown that every eighth woman is suffering from it. This research has been done with the intent to predict the occurrence of breast cancer with the help of various machine learning algorithms. For the analysis purpose, three different datasets were utilized, Wisconsin Breast Cancer (WBC) dataset, Wisconsin Prognosis Breast Cancer (WPBC), and Wisconsin Diagnosis Breast Cancer (WDBC) dataset. Also, the classification used for these datasets was done using Hierarchical Decision Tree (HIDER), PSO, and Genetic Algorithm for Neural Network (GANN). The results were compared based on the accuracy achieved was found that HIDER showed the best results with WBC Dataset, while GANN was the most accurate one with WDBC and WPBC datasets. This research would help organizations, working in the health sector, especially in cancer studies, to predict breast cancer accuracy with accuracy and help cure it in the early stages.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Breastcancer.org. (2016, 12 May). Knowing your risk can save your life [Internet]. Breastcancer.org. 2016.
Aalaei, S., Shahraki, H., Rowhanimanesh, A., & Eslami S. (2016, May). Feature selection using genetic algorithm for breast cancer diagnosis: Experiment on three different datasets. Iranian Journal of Basic Medical Sciences,19(5), 476–482.
Wu, X. (1996). A Bayesian Discretizer for real-valued attributes. The Computer Journal,39(8), 688–691.
Patankar, V., Nawgaje, D., & Kanphade, R. A implementation of ACO technique for cancer diagnosis. International Journal of Current Engineering and Technology E-ISSN 2277–4106, P-ISSN 2347–5161 ©2014 INPRESSCO.
Shah, C., & Jivani, A. G. (2013, 4–6 July) Comparison of Data Mining Classification Algorithms for Breast Cancer Prediction, 4th ICCCNT 2013. Tiruchengode, India.
Claridge, F. B., Iqbal, M., & Zhang, M. Evolutionary algorithms for classification of mammographic densities using local binary patterns and statistical features, 978–1–5090–0623–6/16/$31.00_c 2016 IEEE.
Narwal, M., & Mittal, P. (2012, June). Keel a data mining tool: Analysis with genetic. IJCSMS International Journal of Computer Science & Management Studies, 12 ISSN (Online), 2231–5268.
Isaac, L. D., & Kumar, S. Diagnosis Prognosis and Prevention of Breast Cancer based on present scenario of human life, 978–1–5386–2051–9/18/$31.00 ©2018 IEEE.
Ghosh, S., Hossain, J., Fattah, S. A., & Shahnaz, C. Efficient approaches for accuracy improvement breast cancer classification using Wisconsin database. 978–1–5386–2175–2/17/$31.00 ©2017 IEEE.
Elouedi, H., Meliani, W., Elouedi, Z., & Amor, N. B. A Hybrid Approach Based on Decision Trees and Clustering for Breast Cancer Classification. 978–1–4799–5934–1/14/$31.00 ©2014 IEEE.
Chug, A., & Malhotra, R. (2016, April). Benchmarking framework for maintainability prediction of open source software using object oriented metrics. ICIC International c 2016 ISSN 1349–4198, 12(2), 615–634.
UCI Machine Learning Repository: Breast Cancer Wisconsin (Original) Data Set [Internet]. Archive.ics.uci.edu. 2016 [cited 12 May 2016].
UCI Machine Learning Repository: Breast Cancer Wisconsin (Diagnosis) Data Set [Internet]. Archive.ics.uci.edu. 2016 [cited 12 May 2016].
UCI Machine Learning Repository: Breast Cancer Wisconsin (Prognosis) Data Set [Internet]. Archive.ics.uci.edu. 2016 [cited 12 May 2016].
Lanzi, P. L. (1997). Fast feature selection with genetic algorithms: A filter approach. IEEE International Conference on Evolutionary Computation. Indianapolis. Indianapolis (USA), pp. 537–540.
Parpinelli, R. S., Lopes, H. S., & Freitas, A. A. (2002). Data Mining with an ACO Algorithm. IEEE Transactions on Evolutionary Computation,6(4), 321–332.
Sousa, T., Silva, A., & Neves, A. (2004). Particle swarm based data mining algorithms for classification tasks. Parallel Computing,30, 767–783.
Yao, X. (1999). Evolving artificial neural networks. Proceedings of the IEEE,87(9), 1423–1447.
Aguilar-Ruiz, J. S., Giráldez, R., & Riquelme, J. C. (2007). Natural encoding for evolutionary supervised learning. IEEE Transactions on Evolutionary Computation,11(4), 466–479.
Singh, P. D., & Chug, A. Software Defect Prediction Analysis Using Machin Learning Algorithms, 978–1–5090–3519–9/17/$31.00©2017 IEEE.
Dunn, O. (1961). Multiple comparisons among means. Journal of the American Statistical Association,56, 52–64.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Sethi, A., Chug, A. (2021). Breast Cancer Prediction Using Nature Inspired Algorithm. In: Kapur, P.K., Singh, G., Panwar, S. (eds) Advances in Interdisciplinary Research in Engineering and Business Management. Asset Analytics. Springer, Singapore. https://doi.org/10.1007/978-981-16-0037-1_30
Download citation
DOI: https://doi.org/10.1007/978-981-16-0037-1_30
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-0036-4
Online ISBN: 978-981-16-0037-1
eBook Packages: Business and ManagementBusiness and Management (R0)