Abstract
Artificial intelligence techniques and algorithms are applied at various fields such as face recognition, self-driving cars, industrial robots and health care. These real-world conundrums are solved employing artificial intelligence since it focuses on narrow tasks, and AI-driven tasks are very reliable and efficient because of its automated problem-solving techniques. Breast cancer is considered as the most common type of cancer among women. The well-known technique for detection of breast cancer is mammography which can diagnosis anomalies and determine cancerous cells. However, in the present breast cancer screenings, the retrospective studies reveal that approximately 20–40% of breast cancer cases are missed by radiologists. The main objective of the proposed algorithm is to exactly forecast the misclassified malignant cancers employing radial basis function network and decision tree. In order to obtain the effective classification algorithm, this work is compared with three widely employed algorithms, namely K-nearest neighbors, support vector machine and Naive Bayes algorithm, and the proposed algorithm achieves a high accuracy.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Al-Najdawi N, Biltawi M, Tedmori S (2015) Mammogram image visual enhancement, mass segmentation and classification. Appl Soft Comput 35:175–185
Baker JA, Kornguth PJ, Lo JY, Floyd CE Jr (1996) Artificial neural network: improving the quality of breast biopsy recommendations. Radiology 198:131–136
Bursteinas B, Long J (2001) Merging distributed classifiers. In: 5th World multi conference on systemic, cybernetics and informatics
Domingos PA (2012) A few useful things to know about machine learning. Commun ACM 55(10):78–87. https://doi.org/10.1145/2347736.2347755
Ganatra A, Panchal G, Kosta Y, Gajjar C (2011) Initial classification through back propagation in a neural network following optimization through GA to evaluate the fitness of an algorithm. Int J Comput Sci Inf Technol 3(1):98–116
Gutiérrez PA, Martínez C (2012) Hybrid artificial neural networks: models, algorithms and data. In: International work-conference on artificial neural networks, pp 177–184
Hall L, Chawla N, Bowyer K (1998) Decision tree learning on very large data sets. In: IEEE international conference on systems, man, and cybernetics, vol 3, pp 2579–2584
Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern Part C 3(6):610–621
Levi F, Bosetti C, Lucchini F, Negri E La, Vecchia C (2005) Monitoring the decrease in breast cancer mortality in Europe. Eur J Cancer Prev 14(6):497–502
Magoulas GD, Prentza A (2010) Machine learning in medical applications. In: Paliouras G, Karkaletsis V, Spyrpoulos CD (eds) Machine learning and its applications, lecture notes in computer science, Springer, Berlin, pp 300–307
Mahmoudabadi H, Izadi M, Menhaj MB (2009) A hybrid method for grade estimation using genetic algorithm and neural networks. Comput Geosci 13:91–101
Mehta M, Agrawal R, Rissanen J (1996) SLIQ: a scalable parallel classifier for data mining, IBM Almaden Research Center, CA 95120
Panchal G, Ganatra A (2012) Optimization of neural network parameter using genetic algorithm. Lambert Academic Publishing, Latvia
Rampun A, Morrow PJ, Scotney BW, Winder J (2017) Fully automated breast boundary and pectoral muscle segmentation in mammograms. Elsevier Artif Intell Med 79:28–41
Sundaram M, Ramar K, Arumugam N, Prabin G (2011) Histogram modified local contrast enhancement for mammogram images. Appl Soft Comput 11(8):5809–5816
Tyczynski JE, Plesko I, Aareleid T, Primic-Zakelj M, Dalmas M, Kurtinaitis J, Stengrevics A, Parkin DM (2004) EU member states: mortality declining in young women, but still increasing in the elderly. Int J Cancer 112(6):1056–1064
Vapnik V, Vashist A (2009) A new learning paradigm: learning using privileged information. Neural Netw 22(5–6):544–557
Verma B, Zhang P (2007) A novel—genetic algorithm to find the most significant combination of features in digital mammograms. Appl Soft Comput 7(2):612–625
Vimal S, Kalaivani L, Kaliappan M, Suresh A, Gao XZ, Varatharajan R (2018) Development of secured data transmission using machine learning-based discrete time partially observed Markov model and energy optimization in cognitive radio networks. Neural Comput Appl 1:1. https://doi.org/10.1007/s00521-018-3788-3
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Communicated by Mu-Yen Chen.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Suresh, A., Udendhran, R. & Balamurgan, M. Hybridized neural network and decision tree based classifier for prognostic decision making in breast cancers. Soft Comput 24, 7947–7953 (2020). https://doi.org/10.1007/s00500-019-04066-4
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-019-04066-4