A comparative study of decision tree and support vector machine for breast cancer prediction

Breast cancer remains a leading cause of mortality among women globally, necessitating accurate and early diagnosis techniques. This study explores the effectiveness of Support Vector Machine (SVM) techniques for diagnosing breast cancer, utilizing the Object-Oriented Analysis and Design Method (OOADM) for system development. The research employed the Wisconsin Breast Cancer Dataset from the UCI Machine Learning Repository, comprising ten features. The dataset was divided into 80% for training and 20% for testing the SVM model. Performance metrics such as classification accuracy, Area Under the Curve (AUC), sensitivity, specificity, and precision were used to evaluate the SVM model, which was also compared against a Decision Tree (DT) model. The results indicated that the SVM model achieved superior performance with an accuracy of 94%, AUC of 98%, sensitivity of 95%, specificity of 87%, and precision of 93%. In comparison, the DT model showed an accuracy of 89%, AUC of 95%, sensitivity of 90%, specificity of 85%, and precision of 90%. The findings underscore the potential of SVM in enhancing breast cancer diagnostic accuracy, thereby supporting early detection and treatment.