MATHEMATICAL MODEL FOR PREDICTING FASTING BLOOD GLUCOSE LEVEL IN DIABETES MELLITUS PATIENTS.

OBJECTIVE

The aim: To substantiate the use of data on patients' lifestyle, parameters of blood glucose, heart rate, blood pressure and bread units to build a mathematical model for predicting fasting blood glucose level in diabetes mellitus patients to improve existing measures for diabetes prevention.

PATIENTS AND METHODS

Materials and methods: An open database consisting of the studied parameters of 359 people was used in the research. The linear regression method was used to predict fasting blood glucose level in diabetes mellitus patients. The statistical software IBM SPSS Statistics Version 23 was chosen for calculations.

RESULTS

Results: To calculate the coefficients of the linear regression equation, stepwise elimination of parameters was chosen. The analysis of the coefficients of influence of independent variables on dependent showed that the greatest effect on the change in glucose level had value of consumed bread units. The model for women diagnosed with type 2 diabetes showed the highest accuracy.

CONCLUSION

Conclusions: Mathematical modeling made it clear that any malnutrition or health disorders can lead to a significant change in glucose levels. The obtained models consist of a number of parameters, some of which might depend on the presence of concomitant diseases. Further studies should focus on the optimal combination of various parameters taking into account methods of treating comorbidities.