Analysis and predicting the Wisconsin disease by using Machine Learning Algorithm

Abstract

Computer vision should be widely used in medical applications, such as determining the sort of cancerous cells. Carcinoma is among the most frequent malignancies, and it kills a lot of people every year. It is the most frequent malignancy in females and the leading cause of death in women around the world. Cancerous cells were classified as either malignant (M) or benign (B)  Some of the approaches used to classify and forecast carcinoma include the Decision Tree (CART), Naive Bayes (NB), k Nearest Neighbors (KNN)Support Vector Machine (SVM). A work, an (SVM) on the Wisconsin Carcinoma database was used. The dataset was additionally trained using the KNN, Naive Bayes, and CART methods, with predictive performance for each approach compared.