Breast Cancer Prediction Using Machine Learning

Worldwide, Breast cancer remains one of the primary reasons of female mortality. Detecting breast cancer is important because it gives us time to treat it and get cures. A contrastive study of various machine learning algorithms for breast cancer classification using the widely recognized Wisconsin Breast Cancer Dataset (WBCD) is presented in this study. Functionality of various machine learning models like Support Vector Machine (SVM), Logistic Regression, and Decision Tree is evaluated and compared with standard performance metrics such as accuracy, precision, recall and other related metrics in this research. This analysis yielded that the SVM and Logistics Regression algorithms achieve much higher levels of classification accuracy and reliability, compared to other algorithms. These findings highlight the remarkable usefulness of SVM and Logistic Regression as powerful tools for machine learning based early diagnosis of breast cancers and provide promise for healthcare practitioners and other researchers in the battle against this rampant disease.