Heterogeneous Classifiers with Virtual Flows in Intelligent Systems for Predicting Cardiovascular Complications during the Rehabilitation Period

Virtual models of “weak” classifiers for intelligent systems classifying the risk of recurrent myocardial infarction are considered. Bioimpedance investigation results were included in the construction of models of the risk of cardiovascular complications as an additional risk factor. Five classifiers were studied, of which four were heterogeneous. The models of heterogeneous classifiers were obtained by sequentially increasing the number of decision modules used in the classification model. Use of all decision modules in a heterogeneous classifier gave diagnostic sensitivity of 0.90 with diagnostic specificity of 0.86. When a configuration of the attribute space including only the conventional risk factors was used, the classification quality parameters were no worse than those for known risk scales for cardiovascular complications.