H-BwoaSvm: A Hybrid Model for Classification and Feature Selection of Mammography Screening Behavior Data

Breast cancer is one of the most common cancer in the world. Early detection of cancers cause significantly reduce in morbidity rate and treatment costs. Mammography is a known effective diagnosis method of breast cancer. A way for mammography screening behavior identification is women's awareness evaluation for participating in mammography screening programs. Todays, intelligence systems could identify main factors on specific incident. These could help to the experts in the wide range of areas specially health scopes such as prevention, diagnosis and treatment. In this paper we use a hybrid model called H-BwoaSvm which BWOA is used for detecting effective factors on mammography screening behavior and SVM for classification. Our model is applied on a data set which collected from a segmental analytical descriptive study on 2256 women. Proposed model is operated on data set with 82.27 and 98.89 percent accuracy and select effective features on mammography screening behavior.