Performance Analysis of an ANN-based model for Breast Cancer Classification using Wisconsin Dataset

Breast Cancer (BC) is counted as one of the most fatal diseases responsible for mortality in women. BC feels like a lump that can be felt different than other BC tissues. Cancer cells spread to other parts of the body by getting into the bloodstream or lymphatic system, which results in BC. It becomes crucial to develop and analyze methods to help understand BC and its trend affecting women. In this paper, we discussed different models to analyze BC tumors and classified them into benign and malignant. Benign tumors are referred to as ‘non-cancerous’ by many, and those that are ‘cancerous’ as malignant. The analysis was done on Wisconsin Breast Cancer Dataset (WBCD). We performed Logistic Regression (LR), Decision Tree(DT), Random Forest(RF), K-Nearest Neighbor (KNN), and Artificial Neural Network(ANN) on the dataset and compared their results. Our ANN model yielded the best result among many. All models were compared on various metrics which are Accuracy score, F1 score, Precision score, and Recall score.