Heterogeneous Ensemble Classifier in Medical Decision Support Systems

The work is devoted to solving the current scientific and technical problem of constructing a diagnostic decision support system in medicine based on a heterogeneous ensemble classifier model that implements two approaches to formulating a diagnostic conclusion: a probabilistic one based on the analysis of the training sample, and expert information on the structure of symptom complexes. The choice of prototype matching method as a probabilistic component is justified. Formalization of expert information on the structure of symptom complexes was carried out by representing symptom complexes of diseases with numerical intervals of linguistic variables. Options for taking into account expert assessments about the structure of symptom complexes in an ensemble classifier are considered. Test verification of the developed classifier was done on real medical data and confirmed the effectiveness of its work.