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Optimized Prediction Model to Diagnose Breast Cancer Risk and Its Management

Publication Type : Book Chapter

Publisher : Inventive Communication and Computational Technologies, Springer

Authors : Dr. Manju B. R., Vinod, Athira;

Source : Inventive Communication and Computational Technologies, Springer, Volume 89, p.503–515 (2020)

Url : https://link.springer.com/chapter/10.1007/978-981-15-0146-3_48

Keywords : breast cancer classification, Support vector machine (SVM)

Campus : Amritapuri

School : School of Arts and Sciences

Department : Mathematics

Year : 2020

Abstract : Breast malignancy is the second biggest disease that results in fatal condition for women population. Research endeavors have revealed with expanding affirmation that the support vector machines (SVMs) have more noteworthy precise conclusion capacity. In this paper, breast disease determination is dependent on a SVM-based technique that has been proposed. Investigations have been directed on various preparing test allotments of the Wisconsin breast malignancy dataset (WBCD), which is generally utilized among scientists who use machine learning strategies for breast disease conclusion. The working of the technique is assessed by utilizing characterization precision, particularity positive and negative prescient qualities, collector working trademark bends, and perplexity lattice. The outcomes demonstrate that the most elevated grouping precision (99%) is achieved for the SVM.

Cite this Research Publication : A. Vinod and Manju B. R., “Optimized Prediction Model to Diagnose Breast Cancer Risk and Its Management”, in Inventive Communication and Computational Technologies, vol. 89, Springer, 2020, pp. 503–515.

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