Ovarian Cancer Detection Method Using Proteomic Patterns, Independent Component Analysis, and Support Vector Machine - Araujo, Wesley B. Dominices de; Campos, Lúcio F. A.; Furtado, Aline S. (Proceedings of the Brazilian Symposium on Computing Applied to Health (SBCAS), 2014)
It is proposed a CAD method to detect ovarian cancer, using Independent Component Analysis, the technique of Maximum Relevance and Minimum Redundancy, to reduce dimensionality and the computational cost, and Support Vector Machine, for classification of samples between presence or absence of cancer. The method was tested with a proteomic patterns set from SELDI-TOF database, and best performance was achieved with 10 features vector, resulting 98.80% of accuracy, with 95.65% of specificity and 100% of sensitivity.