EXPLORING THE APPLICATION OF MACHINE LEARNING STRATEGIES AND TOOLS IN THE DIAGNOSIS AND PROGNOSIS OF BREAST CANCER

Raj Verma

Vol. 4, Jan-Dec 2018

Page Number: 153 - 159

Abstract:

One of the major widespread disease in these days for women is breast cancer. right treatment and early detection is a major step to take to prevent this disease. however, it's not easy, because of a few vulnerabilities and recognition of mammograms. Machine Learning (ML) strategies can be utilized to create apparatuses for doctors that can be utilized as a compelling system for early location and conclusion of breast cancer growth which will significantly improve the survival rate of patients. This paper analyses about three of the most prominent ML strategies generally utilized for breast cancer disease location and finding, in particular Support Vector Machine (SVM), Random Forest (RF) and Bayesian Networks (BN). The Wisconsin breast cancer malignancy informational collection was utilized as a preparation set to assess and think about the execution of the three ML classifiers as far as key parameters, for example, exactness, review, accuracy and zone of ROC. The outcomes got in this paper give an outline of the condition of craftsmanship ML methods for bosom growth identification.

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