USING NEURAL NETWORK MODELING TO PREDICT THE COURSE OF ACUTE PANCREATITIS

Abstract

The database of studies of 82 patients with acute pancreatitis are presented. Using neural network analysis, the most indicative parameters for predicting acute pancreatitis were revealed: indexes of Kalf-Kalif intoxication modified by Kostyuchenko and Khomich, Reis, Garkavi, the ratio of leukocytes to ESR, leukocyte index, general intoxication index; sonographic parameters – the size of the head of the pancreas, the diameter of the splenic vein, the presence of free fluid in the abdominal cavity; biochemical parameters – blood amylase concentration, urine diastase. When conducting clustering in a multidimensional feature space, a Kohonen neural network was created. All analyzed objects were effectively divided into 3 clusters. The most severe and prognostically unfavorable is cluster 1, which included data from 30 patients, with the maximum mortality rate and maximum hospital stay.