Tuning to Optimize SVM Approach for Breast Cancer Diagnosis with Blood Analysis Data

Abstract

As one of the most prevalent cancers among women worldwide, breast cancer has attracted the most attention by researchers. It has been verified that an accurate and early detection of breast cancer can increase the chances for the patients to take the right treatment plan and survive for a long time. This paper intents the scope of the biomarker that can be used to predict the breast cancer from the routine Blood Analysis Data. Support vector machine (SVM) has already made an important contribution to the field of cancer classification. However, different kernel function configurations and their parameters will significantly affect the performance of SVM classifier. To improve the classification accuracy of SVM classifier for breast cancer diagnosis, this paper proposed a novel cancer classification algorithm based on optimizes the relevant parameters of SVM classifier through an intelligent algorithm using Grid Search Algorithm These parameters are: Gaussian radial basis function (GRBF) kernel parameter g and C penalty parameter of SVM classifier. Our experiment showed that SVM parameter optimization using grid search always finds near optimal parameter combination within the given ranges to evaluate the performance of the proposed model, Breast Cancer Coimbra Dataset used taken from UCI library. In this dataset age, body mass index (BMI), glucose, insulin, homeostasis model assessment (HOMA), leptin, adiponectin, resistin and chemokine monocyte chemoattractant protein 1 (MCP1) attributes were used. The performance of the proposed method is compared with that of other methods on this data sets. The obtained results show the improvement over state of-the-art algorithms with improved performance parameters e.g. disease prediction accuracy, sensitivity and better F1 score etc.. Funding Statement: The authors stated that this research received no external funding. Declaration of Interests: The authors declare no conflict of interest. Ethics Approval Statement: Not required.