Breast Cancer Prediction Using Machine Learning

Abstract

Breast cancer may be a prevalent explanation for death, and it's the sole sort of cancer that's widespread among women worldwide. The prime objective of this paper creates the model for predicting breast cancer using various machine learning classification algorithms like k Nearest Neighbor (kNN), Support Vector Machine (SVM), Logistic Regression (LR), and Gaussian Naive Bayes (NB). And furthermore, assess and compare the performance of the varied classifiers as far as accuracy, precision, recall, f1-Score, and Jaccard index. The breast cancer dataset is publicly available on the UCI Machine Learning Repository and therefore the implementation phase dataset is going to be partitioned as 80% for the training phase and 20% for the testing phase then apply the machine learning algorithms. k Nearest Neighbors achieved a significant performance in respect of all parameters.