SVD based Monte Carlo approach to feature selection for early ovarian cancer detection Shufei Chen+, Bin Han+, Lihua Li*, Lei Zhu, Haifeng Lai, Qi Dai Institute for Biomedical Engineering and Instrumentation Hangzhou Dianzi University Hangzhou, {bhan, zhulei, lilh}***@hdu.
Abstract—Ovarian Carcinoma (OvCa) is the most lethal type of and wrapper methods, firstly choose biomarkers in terms of gynecological cancer. The studies show that about 90% patients their correlation with cancer diagnostic es, then fine could be saved if they are treated in the early stage. In this study, tune the selected biomarkers set based on classification a novel biomarker selection approach is proposed which accuracies with a wrapper method. This strategy es combines singular value position (SVD) and Monte Carlo the problem of that the selected biomarkers are dependent on strategy to early OvCa detection. Other than supervised the evaluation function of learning algorithm while using classification methods or differential expression detection based wrapper methods only and avoids the risk of overfitting, as methods, the biomarkers are identified in terms of their well as keeps a higher classification accuracy and ensures the relevance to the clinical es and stability. Comparative selected biomarkers having significant biological relevance. study and st
