Breast Cancer Detection Using Machine Learning

Abstract

Breast tissues swell and grow out of control, generating a lump or fine layer as just cancer. This is how breast cancer arises. The second most widespread cancer in girls, after melanoma, is breast cancer. Women above Fifty are most likely to experience this. [1] However, it can affect anyone at any age. Reducing health risks and boosting prevention strategies may help prevent cancer. Cancer prevention human trials are used to explore strategies for preventing cancer, and novel approaches to avoiding breast cancer are indeed being investigated in clinical studies. In this article, i used machine learning algorithms to determine whether cancer is benign or malignant. If the condition is benign, the doctor can begin therapy right once. For this project, the data is collected by Kaggle. There are four machine learning algorithms used for analysis. They are decision trees, logistic regression, bagging, and random forest. Here, i find out the best algorithm with accuracy and precision, f1-score, recall, and confusion matrix.PCA and correlation matrices are utilized for data visualization. The random forest does have the best performance (98%) for the datasets that was provided and the bagging algorithm gives the best AUC value (99%).