Breast Cancer Diagnosis using Support Vector Machines and Feature Selection
Keywords:
Breast cancer diagnosis; Support vector machines; Feature selection; Wisconsin breast cancer diagnosis dataAbstract
This study investigates the use of support vector machines (SVMs) in conjunction with feature selection for the purpose of breast cancer diagnosis. The aim of the research is to select the most relevant features that contribute to accurate classification and to use them to train the SVM model. The proposed approach is evaluated using a publicly available Wisconsin breast cancer dataset (WBCD) [2] and compared with other classification methods. The results show that the SVM model with feature selection outperforms other classification methods in terms of accuracy and provides a promising approach for breast cancer diagnosis. The study's findings demonstrate that the SVM model, when combined with feature selection, achieved a high classification accuracy of 98.25%. This model utilized only nine features. The accuracy achieved by the SVM model also indicates its potential to assist in the early detection of breast cancer, which is crucial in improving patient outcomes.
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