Support Vector Machine Based Supervised Machine Learning Algorithm for Finding ROC and LDA Region

Authors

  • Nalli Vinaya Kumari
  • G. S. Pradeep Ghantasala

DOI:

https://doi.org/10.37591/joosdt.v7i1.2431

Abstract

Breast Cancer is one of the most dangerous forms of conditions in the world, according to the Breast Cancer Institute (BCI). Breast cancer is a leading cause of death for women. Several empirical studies have explored the use of machine and predictive systems for breast cancer. Breast cancer can be the major cause of women’s death. Many claim their algorithms are faster, easier or more precise, as according to cancer. Net, for the most cancer groups and associated hereditary syndromes individualized literature exhibits over 100 and 20 varieties. The purpose of this study was to optimize the algorithm used to carry out the study, including hybrid models mixed from various algorithms for Machine Learning (ML), including Vector Machine (SVM), to K-Nearest Neighbor (KNN) for efficient breast cancer detection. Two kinds of tumors exist. One is a brain tumor, and the other is malignant, benign tumors which are not primarily types of cancer and malignant tumors.

Keywords: Accuracy, Gaussian blend show, SVM, KNN, ROC, Wisconsin breast cancer

Cite this Article: Nalli Vinaya Kumari, G. S. Pradeep Ghantasala. Support Vector Machine Based Supervised Machine Learning Algorithm for Finding ROC and LDA Region. Journal of Operating Systems Development & Trends. 2020; 7(1): 26–33p.

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Published

2020-06-02

Issue

Vol. 7 No. 1 (2020)

Section

Research Articles

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