Background: Breast cancer is the development of a malignant tumor in the breast of human
beings (especially females). If not detected at the initial stages, it can substantially lead to an
inoperable construct. It is a reason for the majority of cancer-related deaths throughout the world.
Objectives: The main aim of this study is to diagnose breast cancer at an early stage so that the required
treatment can be provided for survival. The tumor is classified as malignant or benign accurately
at an early stage using a novel approach that includes an ensemble of the Genetic Algorithm
for feature selection and kernel selection for SVM-Classifier.
Methods: The proposed GA-SVM (Genetic Algorithm – Support Vector Machine) algorithm in this
paper optimally selects the most appropriate features for training with the SVM classifier. Genetic
Programming is used to select the features and the kernel for the SVM classifier. The Genetic Algorithm
operates by exploring the optimal layout of features for breast cancer, thus, subjugating the
problems faced in exponentially immense feature space.
Results: The proposed approach accounts for a mean accuracy of 98.82% by using the Wisconsin
Diagnostic Breast Cancer (WDBC) dataset available on UCI with the training and testing ratio being
Conclusion: The results prove that the proposed model outperforms the previously designed models
for breast cancer diagnosis. The outcome assures that the GA-SVM model may be used as an effective
tool in assisting the doctors in treating the patients. Alternatively, it may be utilized as an alternate
opinion in their eventual diagnosis.