On Orthogonal Feature Extraction model with applications in medical prognosis

Microarray-based gene expression profiling for cancer prognosis can shed light on providing prognostic information and it has statistically become a highly reliable technique. From a computer scientist's point of view, cancer prognosis can be termed as a classification problem. In microarray-based cancer prognosis problems, selection of critical attributes is extremely popular as a lot of features are irrelevant or redundant. Appropriate feature selection not only can filter irrelevant features but also improve prediction accuracy. Various models and methods have been developed to rank the importance of features. However, there is no dominant strategy because all methods have their own advantages and drawbacks. In this paper, we propose an Orthogonal Feature Extraction (OFE) model based on feature ranking techniques, which aims at improving cancer prediction accuracy. Our proposed model is compared with the most widely used feature extraction method: Principal Component Analysis (PCA), and two other existing feature extraction methods: Neighborhood Component Analysis (NCA) and Linear Discriminant Analysis (LDA) through 5-fold cross-validation. Numerical results indicated that OFE method can efficiently construct combinations of significant variables enabling computational complexity reduction and also provide recognizable better performance.