A Hybrid Machine Learning Approach for Breast Cancer Detection

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Neha Kumari, Khusboo Verma

Abstract

Now a day, cancer is one of most common and internecine disease among all disease present in the world. A cancer disease is classified into different types based on the body location like breast cancer, kidney cancer, liver cancer etc. but breast cancer is one of the most common cancer in woman and 8% of woman were diagnosed breast cancer in 2016. It is found that if the cancer is diagnosed in the early stage than the probability of the survival is higher. Now a day, Machine learning play a vital role in order to detect cancer in the early stage. Lots of work has been done previously which uses machine learning approach like support vector machine, Naïve Bayes, logistic regression etc. In this paper w proposed hybrid approach for detecting cancer in the early stage. This hybrid approach is the combination of Support vector machine and Naïve Bayes approach. In order to evaluate the performance of the proposed approach uses Wisconsin Breast Cancer (WBC) which is downloaded from the UCI machine learning repository. Performance of the proposed approach is measured in term of accuracy, F- value. Experiment results shows that proposed approach gives better result as compare to the competitive approach.

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