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Breast Cancer Data Prediction by Dimensionality Reduction Using PCA and Adaptive Neuro Evolution

Breast Cancer Data Prediction by Dimensionality Reduction Using PCA and Adaptive Neuro Evolution

R. R. Janghel, Ritu Tiwari, Rahul Kala, Anupam Shukla
Copyright: © 2012 |Volume: 3 |Issue: 1 |Pages: 9
ISSN: 1941-868X|EISSN: 1941-8698|EISBN13: 9781466612730|DOI: 10.4018/jissc.2012010101
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MLA

Janghel, R. R., et al. "Breast Cancer Data Prediction by Dimensionality Reduction Using PCA and Adaptive Neuro Evolution." IJISSC vol.3, no.1 2012: pp.1-9. http://doi.org/10.4018/jissc.2012010101

APA

Janghel, R. R., Tiwari, R., Kala, R., & Shukla, A. (2012). Breast Cancer Data Prediction by Dimensionality Reduction Using PCA and Adaptive Neuro Evolution. International Journal of Information Systems and Social Change (IJISSC), 3(1), 1-9. http://doi.org/10.4018/jissc.2012010101

Chicago

Janghel, R. R., et al. "Breast Cancer Data Prediction by Dimensionality Reduction Using PCA and Adaptive Neuro Evolution," International Journal of Information Systems and Social Change (IJISSC) 3, no.1: 1-9. http://doi.org/10.4018/jissc.2012010101

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Abstract

In this paper a new approach for the prediction of breast cancer has been made by reducing the features of the data set using PCA (principal component analysis) technique and prediction results by simulating different models namely SANE (Symbiotic, Adaptive Neuro-evolution), Modular neural network, Fixed architecture evolutionary neural network (F-ENN), and Variable Architecture evolutionary neural network (V-ENN). The dimensionality reduction of the inputs achieved by PCA technique to an extent of 33% and further different models of the soft computing technique simulated and tested based on efficiency to find the optimum model. The SANE model includes maximum number of connections per neuron as 24, evolutionary population size of 1000, maximum neurons in hidden layer as 12, SANE elite value of 200, mutation rate of 0.2, and number of generations as 100. The simulated results reflect that this is the best model for the prediction of the breast cancer disease among the other models considered in the experiment and it can effectively assist the doctors for taking the diagnosis results as its efficiency found to be 98.52% accuracy which is highest.

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