SVD Based Monte Carlo Approach to Feature Selection for Early Ovarian Cancer Detection

Ovarian Carcinoma (OvCa) is the most lethal type of gynecological cancer. The studies show that about 90% patients could be saved if they are treated in the early stage. In this study, a novel biomarker selection approach is proposed which combines singular value decomposition (SVD) and Monte Carlo strategy to early OvCa detection. Other than supervised classification methods or differential expression detection based methods, the biomarkers are identified in terms of their relevance to the clinical outcomes and stability. Comparative study and statistical analysis show that the proposed method outperforms SVM-RFE and T-test methods which are the typical supervised classification and differential expression detection based feature selection methods in feature set stability and achieve satisfying classification result (88.9%) as well. The reliability of the identified biomarkers is also biologically validated and supported by other biological research.