A Cost Sensitive SVM and Neural Network Ensemble Model for Breast Cancer Classification

Tina Elizabeth Mathew


Breast Cancer has surpassed all categories of cancer in incidence and is the most prevalent form of cancer in women worldwide. The global incidence rate is seen to be highest in the country of Belgium as per statistics of WHO. In the case of developing countries specifically, India, it has overtaken other cancers and stands first in incidence and mortality. Major factors identified as impacting the prognosis and survival in the country is chiefly the late diagnosis of the disease and diverse situations prevailing in different parts of the country including lack of diagnostic facilities, lack of awareness, fear of undergoing existing procedures and so on. This is also true for many other countries in the world. Early diagnosis is a vital factor for survival. The implementation of machine learning techniques in cancer prediction, diagnosis and classification can assist medical practitioners as a supplementary diagnostic tool. In this work, an ensemble model of a polynomial kernel-based Support Vector machines and Gradient Descent with Momentum Back Propagation Artificial Neural Networks for Breast Cancer Classification is proposed. Feature selection is applied using Genetic Search for identifying the best feature set and data sampling techniques such as combination of oversampling and undersampling and cost senstivke learning are applied on the individual Neural Network and Support Vector Machine classifiers to deal with issues related with class imbalance. The ensemble model is seen to show superior performance in comparison with other models producing an accuracy of 99.12%.


Breast cancer; Classification; Cost Sensitive Learning; Neural Networks; Support Vector Machines


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