Breast Cancer Using Machine Learning

Abstract

Abstract: Women die from breast cancer, which is an abstract concept. Breast cancer is the most important problem. The most frequent cancer in women diagnosed globally has now surpassed lung cancer in prevalence. early detection that aids in cancer prevention. If breast cancer is to have a very high survival rate, it must be found in its earliest stages. The efficient machine learning method is utilized to categorize the data. Methods are employed in the medical field to aid in diagnosis and decisionmaking. This study used the Wilcoxon breast cancer dataset to do data visualization and compare various machine learning methods, including the Support Vector Machine (SVM), Decision Trees, Naive Bayes (NB), K Nearest Neighbours (K-NN), Adaboost, Xgboost, and Random Forest. The primary goal is to assess the data's accuracy in terms of each algorithm's efficiency and effectiveness in terms of accuracy, precision, sensitivity, and specificity. Our goal is to use machine learning to detect things quickly, effectively, and precisely. The experimental findings had the lowest error rate and the best accuracy (98.24%).