Insights into breast cancer: A simple machine learning method for early disease detection

Abstract

Breast cancer remains a significant global health challenge, where accurate prediction plays a vital role in early diagnosis and effective treatment, ultimately saving lives. This study evaluates the performance of three machine learning models; Support Vector Machine (SVM), Random Forest Classifier, and XGBoost for breast cancer prediction. Using the Wisconsin Breast Cancer Dataset, the models were assessed based on their accuracy. The experimental results demonstrated that SVM outperformed the other models, while both XGBoost and the Random Forest Classifier achieved just slightly lower accuracies. This research underscores the potential of machine learning models in enhancing breast cancer prediction and highlights their importance in advancing early detection and treatment strategies.