A Review of the Automatic Methods of Cancer Detection in Terms of Accuracy, Speed, Error, and the Number of Properties (Case Study: Breast Cancer)

Abstract

The purpose of this article is areview of the automatic methods of cancer detection in terms of accuracy speed error and the number of properties and we have selected the breast cancer as the subject of the case study The data used in this academic study area courtesy of the UCI in California This database is called The Wisconsin Breast Cancer Datasets and includes699 data units divided into benign and malignant classes Ten properties wereassigned to each datum Four types of algorithmsare used in this article namely classification algorithms vector machine algorithms neural networks algorithms and data mining algorithms Each category was evaluated separately and the best method in each category was identified in terms of accuracy speed error and the number of properties