Comparison of Classifiers with Application on DCE-MRI and Preterm Birth

Abstract

Abstract: Cervical cancer and Preterm birth in a pregnant woman is the most serious issues. The aim of our study was to compare different classifiers with SVM and its application. Various statistical methods are proposed to predict special factors. In this probability distribution is used to predict the desired outcome. However, most of the times enough information about the probability distribution is not available. In such situation, we need well predictors with minimum assumptions. Support vector machine (SVM) is a good statistical method for prediction. First order statistical features of the Brix parameters were used. First and second order features were used as explanatory variables for support vector machine (SVM) classification. Second order GLCM features could significantly predict treatment outcome with more accuracies. The performance of the classifiers is measured in terms of accuracy, specificity, and sensitivity. Finally, the result indicates the SVM classifier generates highest level accuracy, which is higher than other classifiers.