Systems and Means of Informatics
2017, Volume 27, Issue 3, pp 63-73
PREDICTION OF LATE POSTOPERATIVE COMPLICATIONS BASED ON THE RESULTS OF DISCRIMINANT AND CORRELATION ANALYSES OF THE EARLY POSTOPERATIVE GLYCEMIA CHARACTERISTICS
- T. V. Zakharova
- A. V. Slivkina
Abstract
The application of the statistical methods for multidimensional observations classification, including discriminant analysis and data correlation analysis which make it possible to evaluate the strength of the factors' joint influence on the result, is considered. The main task is to predict complications development among patients, who underwent pancreas surgery. The research hypothesis suggests that the average early postoperative blood glucose level is of a crucial value for predicting carbohydrate metabolism disorders in the late postoperative period. The considered solution is based on a set of predictors (variables) associated with the blood glucose level, i. e., variance, sample size, mean, maximum, and minimum. The results of the discriminant analysis, performed using the STATISTICA software package on the basis of the available experimental data, do not confirm the research hypothesis.
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[+] About this article
Title
PREDICTION OF LATE POSTOPERATIVE COMPLICATIONS BASED ON THE RESULTS OF DISCRIMINANT AND CORRELATION ANALYSES OF THE EARLY POSTOPERATIVE GLYCEMIA CHARACTERISTICS
Journal
Systems and Means of Informatics
Volume 27, Issue 3, pp 63-73
Cover Date
2017-09-30
DOI
10.14357/08696527170306
Print ISSN
0869-6527
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
Lambda Wilks; data sample; sample variance; range; discriminant analysis
Authors
T. V. Zakharova  , 
and A. V. Slivkina
Author Affiliations
Department of Mathematical Statistics, Faculty of Computational Mathematics and Cybernetics, M.V. Lomonosov Moscow State University, 1-52 Leninskiye Gory, GSP-1, Moscow 119991, Russian Federation
Institute of Informatics Problems, Federal Research Center "Computer Science
and Control", Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
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