Hybrid Methods For Classification Of Breast Cancer Using Machine Learning Techniques

Over 10% of population in the world is affected by breast cancer and it is prominent to predict and classify the type and seriousness of the disease. Considering the risks involved, early an accurate diagnosis of breast cancer plays an important role in its treatment. This paper aims to help the healthcare system worldwide by providing with a dependable method for the prediction of breast cancer. Various ML methods are proposed to find the accuracy and precision of the disease from mammography images and efforts to improve levels of accuracy has been taken. In this paper, 5 ML methods are compared to find the accuracy of Breast Cancer and further voting methods have been used to select the best possible combinations to find precision, F-score, recall and accuracy to find the type of cancer in both men and women. From the results, it is evident that algorithms that are based on distance have better results compared to methods based on entropy of the dataset for the applied dataset.