الفهرس 
Chapter 1 IntroductionOnly 14 pages are availabe for public view
 |  | from | 90 |  |  |
| | | from | 90 | | |
Abstract
One of the most top diseases nowadays and rapidly spreading disease in the
world is breast cancer that causes death for many women over the world. Most
cases of breast cancer are observed in females. Breast cancer can be controlled
with early detection. Early discovery helps to manage a lot of cases and lower
the death rate. On breast cancer, numerous studies have been conducted.
Breast Cancer consists of many types. Artificial intelligence has an effect role in
detecting and classification the breast cancer. We need to classify these types so
we used Machine Learning using feature selection and Deep Learning.
In Machine Learning work we used 13 classification methods like Support
Vector Machine ,AdaBoost, Gaussian NB,Dummy, K-Neighbors ,Random
Forest,Logistic Regression,NuSVM,Linear SVM,SGD,MLP,Gaussian Processes
and Decision Tree classifiers. . This work is evaluated using three keys
accuracy, cross validation score and execution time. The results detect that
Linear SVM Support Vector Machine achieved high accuracy (98.25%) and
Random Forest and AdaBoost achieved high cross validation score (97.01%)
when compared with other classification methods. Whereas Gaussian NB
classifier achieved minimum execution time (0.01 seconds). A data set with 31
feature and 570 records are used for testing the algorithms. 20% of data set will
be used in testing and 80% for training