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DOI: 10.31071/kit2017.13.05

Inventory reference ISSN 1812-7231 Klin.inform.telemed. Volume 12, Issue 13, 2017, Pages 35–41

Author(s) A.V.Frolov1, M.A.Martsennyuk2, T.G.Vaikhanskaya1, V.B.Poljakov2

Institution(s) 1Republican Scientific and Practical Centre "Cardiology", Minsk, Belarus 2Perm State National Research University, Perm, Russia

Article title Fuzzy logic in prediction of adverse scenarios of the cardiac diseases

Abstract (resume)

Introduction. Prediction of adverse scenarios of the diseases of the cardiovascular system has an important clinical significance. A number of effective models based on Cox proportional hazard method have been created. However, with dichotomous variables and hardware errors in the neighborhood of the threshold values, the probability of an erroneous forecast is high. The method of fuzzy logic is able to reduce this disadvantage.

Purpose. Development of the model for individualized risk-stratification of patients based on fuzzy logic and its testing in patients with chronic heart failure.

Material and methods of the study. The risk-stratification model is based on the Cox logit-regression method in combination with the fuzzy logic. The model was tested in 240 patients with chronic heart failure. The digital ECG-study with an assessment of markers of the myocardium electrical instability, 24-hour holter monitoring and echocardiography were performed.

Results of the study. The probability function of adverse cardiovascular events in dichotomous variables was investigated. Its spasmodic character is revealed. The Cox logit regression is proposed to be supplemented with fuzzy sets in the form of sigmoidal membership functions. The model was tested in 240 patients with chronic heart failure. The sensitivity of the developed model of individual risk-stratification was 81%, and its specificity 99%.

Conclusions. Fuzzy logic allowed to reduce the jumps in assessing the probability of adverse events near threshold values, which favorably affected on the accuracy of the prognosis. The personal prognostic model developed by us has 94% predictive accuracy.

Keywords ECG, electrical instability of the myocardium, risk-stratification, logit regression, fuzzy logic


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