Method for Determining the Body's Level of Protection According to Oxidant Status in Assessing the Influence of Industrial Risk Factors on Health.

This work aims at improving the quality of health assessments, specifically under the influence of occupational risk factors. For this purpose, additional informative indicators are utilized in prognostic and diagnostic models. The models are used to characterize the level of body protection based on oxidative status. A quantitative method is proposed to assess the body's level of protection by means of the levels of lipid peroxidation and antioxidant activity, which characterize the body's oxidative status. A mechanism is developed for integrating the proposed method into prognostic and diagnostic decision rules. The developed rules are in the form of mathematical models used to synthesize hybrid fuzzy decision rules, which are then used to quantify the level of body protection (LBP) against external risk factors, based on the use of protection level functions in terms of lipid peroxidation and antioxidant activity. A mechanism for embedding LBP into predictive and diagnostic decision rules has been proposed. The proposed method is used to predict the occurrence and development of coronary heart disease in railroad locomotive drivers. It was found that to improve the predicting and diagnosing of diseases caused by external pathogenic factors, quantitative assessments of LBP, determined by oxidative status, can be implemented. It has been established that the use of the protection level indicator in predictive decision rules makes it possible to increase the efficiency of the prediction while simultaneously increasing its accuracy.

Development of a Fuzzy Diagnostic Model of Ischemic Disease of the Lower Limbs for Different Stages of Patient Management.

Ischemic disease has severe impact on patients which makes accurate diagnosis vital for health protection. Improving the quality of prediction of patients with ischemic extremity disease by using hybrid fuzzy model allows for early and accurate prognosis of the development of the disease at various stages. The prediction of critical ischemia of lower extremity (CLI) at various disease stages is complex problem due to inter-related factors. We developed hybrid fuzzy decision rules to classify ischemic severity using clinical thinking (natural intelligence) with artificial intelligence, which allows achieving a new quality in solving complex systemic problems and is innovative. In this study mathematical model was developed to classify the risk level of CLI into: subcritical ischemia, favorable outcome, questionable outcome, and unfavorable outcome. The prognosis is made using such complex indicators as confidence that the patient will develop gangrene of the lower extremity (unfavorable outcome), complex coefficient of variability, and reversibility of the ischemic process. Model accuracy was calculated using representative control samples that showed high diagnostic accuracy and specificity characterizing the quality of prediction are 0.9 and higher, which makes it possible to recommend their use in medical practice.