Breast Cancer Prediction: Impact of Stratified Sampling Approach on Classifier Accuracy

Late identification of breast cancer is a major concern in India. After lung cancer, breast cancer is the second most common cause of mortality for women. In the present study, artificial intelligence is utilised to determine if a breast tumour is benign or malignant. The goal of this paper is to offer a thorough investigation of how K Nearest Neighbor (KNN) is used to identify breast cancer. One of the most basic supervised learning-based machine learning algorithms is KNN. Additionally, we have investigated how stratified sampling affects the classifier's accuracy. The proposed work shows that stratified sampling increases accuracy and F1-scores considerably.