Efficient approaches for accuracy improvement of breast cancer classification using wisconsin database

Breast cancer is the second leading cause of death for women all over the world. But early detection and prevention can significantly reduce the chances of death. This paper deals with different statistical and deep learning analysis of Wisconsin Breast Cancer Database for improving the accuracy in detection and classification of breast cancer based on different attributes. Applying Naïve Bayes, SVM, Logistic Regression, KNN, Random Forest, MLP and CNN classifiers, higher accuracy is obtained which is up to 98% to 99%.