Performance analysis of machine learning techniques for the prediction of breast cancer in big data environment

Every year more than a million women are diagnosed with breast cancer that results in the death of more than half of them, due to the delay in diagnosis of the disease. Machine Learning (ML) offers a better approach for the prediction of breast cancer. A feature selection technique, INTERACT is applied to select relevant features for breast cancer diagnosis. The tool used in this project is WEKA 2.3. The classification algorithms, namely Decision tree, Random forest and Support Vector Machine (SVM) are incorporated along with INTERACT technique. The relevant and important features for breast cancer diagnosis are chosen. The dataset used in this project is Wisconsin Diagnostic Breast Cancer (WDBC) subdirectory. The experimental result shows that the SVM outperforms the other classifiers. It improved accuracy of the diagnostic model by using feature selection method.