Heterogeneous ensemble classifier in computer systems of medical diagnostics

The work is devoted to the solution of an actual scientific and technical problem tasks development of a support system for making diagnostic decisions in medicine based on the developed model of a heterogeneous ensemble classifier, which as basic models implements two approaches to development of diagnostic conclusions: probabilistic , based on the analysis of the training sample and expert information on the structure of symptom complexes of each disease. As a probabilistic component, the choice of the method of comparison with the standard, in which the diagnosed conditions of patients are represented by their standards in the space of signs, is justified. The geometric center of the class grouping in the space of diagnostic features is chosen as the benchmark for each class. Expert knowledge about the structure of the symptom complex is formalized by expressing the symptom complex of the disease in the form of numerical intervals of linguistic changes "below the norm", "norm", "above the norm". Various options for taking into account the opinion of experts about the structure of symptom complexes in the ensemble classifier are considered. Test verification of the developed classifier was conducted on real medical data, which confirmed its effectiveness.