A Numerical Approach to Predict Breast Cancer Using Different Machine Learning Algorithms
Rohit Kanauzia, Soumya Upadhyay, Kamal Kumar Gola, Anupriya Kumari, Alok Kumar · 2023
In current era, breast cancer is one of the serious issues for women as compared to all other cancers. Breast cancer occurs when cancer cells form in breast tissues. It can be fat or connective tissue in the breast and difficult to detect at early age. But, as technology grows, number of detection methods have been put forwarded to diagnose breast cancer at early stage such as Mammography. Apart from clinical application, the diagnosis of breast cancer is also been done by using new Data Mining and Machine Learning approaches. ML been exclusively used in medical application especially for the prediction-based detection. Therefore, in proposed work we have opted novel machine learning approach to predict breast cancer disease. The implementation is done by using 4 machine learning algorithms which are (SVM, Naïve Bayes, Random Forest, XGBoost) classifier. The numerical analysis depicts that XGBoost algorithm is most efficient and effective because of the maximum achieved accuracy of 99.12%.