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Cancer prognosis is one of the hot spots in the study of biological information. There have been many studies to cancer prognosis prediction using machine learning methods, which have achieved better results. Among them, the support vector machine (SVM) gets extensive attention as it is suitable to apply in smallsize, high-dimensional data classification questions. However,SVM only performs well in the case where the class distribution is balanced and the input variables are numerical which are unlikely occurred in the medical domain. So in this study, we introduce a new prognosis prediction method based on SVM, which modify the standard SVM models to fit imbalanced class distribution and hybrid type of features. In details, firstly the similarity of features with nominal and numerical type is redefined in kernel function. Secondly synthetic minority oversampling technique (SMOTE) method is adopted to balance class distribution. Lastly the wrapper method SVM-RFE is introduced to select the useful features to improve the prediction performance. A series of experiments are designed and launched to validate the performance. The results have proved the effectiveness of the proposed methods.

Cancer Prognosis Prediction Using SVM for Hybrid Type and Imbalanced Data Sets