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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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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DOI: https://doi.org/10.1007/978-981-13-1132-1_11
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