Abstract: Breast Cancer (BC) is one of the most extensive diseases worldwide. Proper and earlier diagnosis is a critical stage in treatment. Moreover, it is not easy to detect mammograms due to different uncertainties. Machine Learning (ML) approaches can produce tools for doctors that can be utilized as a valuable system for early identification and diagnosis of BC that will significantly improve the survival rate of patients. This article compares three of the most famous ML approaches typically utilized for BC detection and diagnosis, namely Bayesian Networks (BN), Support Vector Machine (SVM), and Random Forest (RF). The Wisconsin actual BC data set was utilized as a training set to review and compare the execution of the three ML classifiers in terms of main parameters such as precision, accuracy, recall, and area of ROC. The effects gained in this article give a summary of the state of art ML approaches for BC detection.
Keywords: Cancer, Breast Cancer, Machine Learning, Deep learning.
Title: Apply Machine Learning Techniques to Detect Breast Cancer
Author: Prokash Sharma
International Journal of Thesis Projects and Dissertations (IJTPD)
Vol. 10, Issue 4, October 2022 - December 2022
Page No: 41-45
Research Publish Journals
Website: www.researchpublish.com
Published Date: 20-October-2022
