Statistical neural networks and support vector machine for the classification of genetic mutations in ovarian cancer

An optimal genetic mutation diagnosis requires proper selection of mutation classifier. This work investigates the performance of different classification, missing value estimation (MVE) and data dimension reduction techniques for the classification of gene expression data for BRCA1, BRCA2 and Sporadic mutations of epithelial ovarian cancer. Bayesian MVE and zero imputation techniques were employed to deal with missing values. Our study showed the better performance of the Bayesian technique. A novel approach is introduced to use generalized regression neural network (GRNN) as genetic mutation classifier which completely outperformed both well established support vector machine and probabilistic neural network.