الفهرس | Only 14 pages are availabe for public view |
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 |