Cascading SVMS as a Tool for Medical Diagnosis Using Multi-class Gene Expression Data

In this paper we propose a novel Support Vector Machines-based architecture for medical diagnosis using multi-class gene expression data. It consists of a pre-processing unit and N-1 sequentially ordered blocks capable of classifying N classes in a cascading manner. Each block embodies both a gene selection and a classification module. It offers the flexibility of constructing block-specific gene expression spaces and hypersurfaces for the discrimination of the different classes. The proposed architecture was applied for medical diagnostic tasks including prostate and lung cancer diagnosis. Its performance was evaluated by using a leave-one-out cross validation approach which avoids the bias introduced by the gene selection process. The results show that it provides high accuracy which in most cases exceeds the accuracy achieved by the popular one-vs-one and one-vs-all SVM combination schemes and Nearest-Neighbor classifiers. The cascading SVMs can be successfully applied as a medical diagnostic tool.

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