Performance Analysis and Comparison of Machine Learning Algorithms for Breast Cancer Dataset

Breast cancer is one of the most commonly found disease in women. In this research Wisconsin breast cancer data is considered. Data preprocessing and feature selection is done on this dataset. Later machine learning algorithms were applied on this dataset for classification of the disease. In machine learning, Classification is one of the most important research area. Classification allocates the given input to a known category. In this paper different machine algorithms like Logistic regression (LR), Decision tree (DT), Support vector machine (SVM), K nearest neighbors (KNN) were implemented on this dataset. The models were trained and tested with k-fold cross validation data. Accuracy and run time execution of each classifier are implemented in python. From the results it can be observed that Decision Tree is giving an accuracy of 90.7%.