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Prediction of the risk of cardiovascular complications with a segmented space of risk factors and synergy channels

To predict coronary heart disease, a basic structure of a multi-agent intelligent system is proposed, which contains “strong” and “weak” classifiers. “Weak” classifiers are divided into four groups, the first of which analyzes data obtained on the basis of traditional risk factors for coronary heart disease, the second - based on the analysis of electrocardiological studies, the third group of “weak” classifiers is designed to diagnose concomitant diseases and syndromes by predictors used the first two groups of agents, and the fourth analyzes environmental risk factors. The intelligent system allows you to manage the decision-making process through a combination of expert assessments, statistical data and current information. Experimental studies of various modifications of the proposed classifier model have been carried out. Analysis of the classification quality indicators in the experimental group of subjects with different ischemic risk indicators showed that taking into account the influence of the introduced risk factors in the control group increases the diagnostic efficiency by seven percent compared to the experimental group, which acts as a background.