Investigating the Prediction of Breast Cancer Diagnosis by Use of Support Vector Machines

Abstract

This study examines the use of support vector machine (SVM) learning algorithms in predictive analytics models for the detection of breast cancer. This study uses the breast cancer Wisconsin dataset and evaluates the model's performance using measures including accuracy, F1-score, precision, and recall. Comparisons are made between the SVM model's performance and those of alternative classification techniques including logistic regression, decision trees, and random forests. The findings demonstrate the usefulness of utilising predictive analytics models, notably the SVM algorithm, for the detection of breast cancer. The SVM model demonstrated significant predictive effectiveness and accuracy, making it a viable choice of tool for clinicians in the early identification and diagnosis of breast cancer.