Fuzzy Mathematical Models for Predicting and Assessing the Severity of Carbon Monoxide Poisoning

The purpose of the research is to improve the quality of classification of the severity of carbon monoxide poisoning through the use of hybrid fuzzy decision rules.

Methods. In the course of these studies, it was shown that modern approaches to classifying the severity of carbon monoxide poisoning are based on assessing the concentration of carbon monoxide in the air of the working area or carboxyhemoglobin in the blood, and there is a significant uncertainty zone between the studied classes of conditions, which reduces the quality of decisions made and makes it difficult to choose adequate treatment regimens In order to reduce the uncertainty zone, it is proposed to increase the number of severity classes and to adequately describe the uncertainty zones, use the methodology for the synthesis of hybrid fuzzy decision rules with a focus on models for assessing the health status of a person changing under the influence of exposure to harmful chemicals.

Results. Using the methodology of synthesis of hybrid fuzzy decision rules, we have obtained fuzzy mathematical models for the allocation of such classes of severity as normal, mild, medium, severe and critical degrees of poisoning with the confidence of decisions made no worse than 0.95, which allows us to accurately assess the health status of patients with the appointment of adequate prevention and treatment regimens.

Conclusion. In this paper, using the methodology of synthesis of hybrid fuzzy decision rules, fuzzy models of classification of the severity of carbon monoxide poisoning with the allocation of classes: normal, mild, medium, severe and critical poisoning are obtained. During the expert evaluation and mathematical modeling, it was shown that the diagnostic sensitivity, specificity and confidence in the decisions made exceeds the value of 0.95, which is a good practical result for this class of problems.

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