Abstract:

Breast Cancer is one of the most severe diseases that is faced by women leading nowhere other than increased death rates in society. The survival rate increases on detecting breast cancer early as better treatment can be provided. So, correct early detection can assist lower breast most cancers mortality prices. Therefore an Automated breast cancer diagnosis has been developed to reduce the time taken for diagnosis and decreases the spread of cancer. This paper presents, Optimized Prediction method for Breast Cancer detection using Machine Learning with different Feature Extracting strategies. In this paper, Three machine learning algorithms are proposed namely Support Vector Machine (SVM), Logistic Regression, and Decision Tree (DT) algorithm on the Wisconsin (Diagnostic) Data Set. Different features are proposed such as texture, morphological entropy based, scale invariant feature transform (SIFT), and elliptic Fourier descriptors (EFDs). These features are passed as input to ML classifiers. Among all algorithms, SVM performed better with the accuracy of about 98.4%.

Description:

ML classifiers, SV, DT, LR, Feature Extracting strategies, Breast Cancer