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

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