Breast Cancer Detection using Machine Learning Algorithms

Abstract

Machine learning employs classification methods on datasets. The Machine Learning repository provided the cancer datasets that were used in this study, which were used for categorization. Breast cancer databases come in two varieties. There are various numbers of characteristics dispersed among these datasets. Breast cancer observes around 14\% of all female cancers. One in every 28 women will develop breast cancer. To analyse patterns in datasets, machine learning algorithms like SVM, KNN, and decision trees are used. Computers are able to ``learn'' from their past mistakes and come up with solutions that are difficult for humans to come up with. According to the study, there are many effective algorithms for analysing the properties of data sets. This study compares and implements several well known classification methods, including Decision Trees, K Nearest Neighbor, SVM, Bayesian Network, and Naive Bayes on the Wisconsin Diagnostic dataset by calculating its classification accuracy, and its sensitivity and specificity value.