Hybrid Classification Model for Biomedical Data Analysis

Abstract

The paper describes a method for constructing a hybrid classification model that allows combining several sources of biological information in order to build a classifier to identify subtypes of complex diseases. The distinctive feature of the method is its adaptive nature, i.e. the ability to build efficient classifiers regardless of data types, as well as a multi-criteria approach to evaluate the effectiveness of a classification. The testing results on real biomedical data showed the advantages of the proposed hybrid model in comparison with individual classifiers.