Evaluation of data mining algorithms for breast cancer prediction

Breast cancer starts in the breast and develops into a tumour as a result of aberrant cell proliferation. One out of every four cancer cases in women worldwide is breast cancer. As the disease advances, survival becomes more difficult, and the woman in Indiawith more than half of breast cancer cases is diagnosed with advanced stages of breast cancer. Three data mining approaches are used in this study to build a model for early breast cancer diagnosis: Decision Tree, Support Vector Machine, and Artificial Neural Network, and their performance are compared to find the best method for breast cancer prediction. These approaches were tested using the Breast Cancer Wisconsin Original Dataset, which is available in the UCI Machine Learning Repository. The K-Nearest Neighbor approach is used to pre-process the data in this study so that missing attribute values can be filled in the dataset. According to the findings and comparison of outcomes, the algorithm Artificial Neural Network outperforms the other two methods for breast cancer prediction.