Monitoring of Kidney Functions, Electrolytes and Volemia Analyzed by Application Example of Artificial Intelligence in Patients with Heart Failure

Abstract

Basal renal function is a predictor of response to diuretic therapy and marker of poor prognosis. Simultaneous changes in renal function, sodium, potassium values and their interdependence are key parameters in addition to volemia for the assessment of cardiorenal balance. In our paper, an analysis of volemia, electrolytes, and renal function in heart failure was performed using an algorithm based on the ANFIS (Adoptive Neural Fuzzy Inference System), an intelligent approach to renal and heart function monitoring. The study included 90 subjects who were divided into two groups: clinical (n-80) and control (n-10). The base is composed of parameters B-type natriuretic peptide (NT-proBNP), sodium (Na), potassium (K), ejection fraction (EF), EPI creatinine-cystatin C formula and ANFIS expert system combined in neural network and fuzzy logic network. The results showed that the overall trend of data verification in the network with NT-proBNP, Na and K that we formed is approximately 15%, with which subjects can be classified according to the severity of hypervolemia, electrolyte disturbance and renal function. NT-proBNP (pg/mL) had the most influence on the EPI creatinine-cystatin C formula. Serum sodium (Na) has the most influence on the ejection fraction (EF).