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Research Article Open Access
Breast cancer is one of the leading cancers for women when compared to all other cancers. It is the second most common cause of cancer death in women. Breast cancer risk in India revealed that 1 in 28 women develop breast cancer during her lifetime. This is higher in urban areas being 1 in 22 in a lifetime compared to rural areas where this risk is relatively much lower being 1 in 60 women developing breast cancer in their lifetime. In India the average age of the high risk group is 43-46 years unlike in the west where women aged 53-57 years are more prone to breast cancer. The aim of this paper is to investigate the performance of different classification techniques. The data breast cancer data with a total 683 rows and 10 columns will be used to test, by using classification accuracy. We analyse the breast Cancer data available from the Wisconsin dataset from UCI machine learning with the aim of developing accurate prediction models for breast cancer using data mining techniques. In this experiment, we compare three classification techniques in Weka software and comparison results show that Sequential Minimal Optimization (SMO) has higher prediction accuracy i.e. 96.2% than IBK and BF Tree methods.
Breast cancer, Classification techniques, Sequential Minimal Optimization (SMO), IBK, BF Tree., Breast Cancer Clinical Trials,Breast Cancer Immunotherapy,Cancer Signaling Array,Breast Cancer Diagnosis