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Computational Intelligence: Theories, Applications and Future Directions - Volume I pp 133–146Cite as

Dimensionality Reduction-Based Breast Cancer Classification Using Machine Learning

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 798))

Abstract

In the field of medical science, achieving accurate diagnosis of disease before its treatment is a significant obstacle. A lot of tests are available, which not only complicates the diagnostic process but also finds difficulty in deriving results. Therefore, computational diagnostic techniques must be introduced with the support of artificial intelligence and machine learning. Breast cancer, being one of the second-leading cause of deaths in women worldwide, demands terminal diagnosis with the higher degree of accuracy. In this proposed work, the primary purpose is to establish and contrast the integrated approaches involving dimensionality reduction, feature ranking, fuzzy logic, and neural networks for the diagnostic evaluation of breast cancer, namely, benign and malignant. However, the adopted approach has been successful in giving the optimal performance to a greater extent, but a maximum accuracy of 96.58% is obtained by the use of principal component analysis and backpropagation neural network.

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Authors and Affiliations

  1. Department of Information Technology, National Institute of Technology, Raipur, 492010, India

    Kuhu Gupta & Rekh Ram Janghel

Authors
  1. Kuhu Gupta
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  2. Rekh Ram Janghel
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Corresponding author

Correspondence to Kuhu Gupta .

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Editors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India

    Nishchal K. Verma

  2. Department of Aerospace Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India

    A. K. Ghosh

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Gupta, K., Janghel, R.R. (2019). Dimensionality Reduction-Based Breast Cancer Classification Using Machine Learning. In: Verma, N., Ghosh, A. (eds) Computational Intelligence: Theories, Applications and Future Directions - Volume I. Advances in Intelligent Systems and Computing, vol 798. Springer, Singapore. https://doi.org/10.1007/978-981-13-1132-1_11

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