Development of automated system for diagnosing liver cirrhosis and selecting the optimal treatment option
DOI:
https://doi.org/10.15587/2312-8372.2016.70228Keywords:
liver cirrhosis, automated system, forecasting, logistic regressionAbstract
This research is devoted to developing the automated system of diagnosing liver cirrhosis, the number of which according to WHO data increases every year.
Despite the large number of existing achievements in the field of mathematical simulation of liver disease, creation of diagnostic models based on simple laboratory evidence is still relevant, because most of them don’t consider the possibility of qualitative signs or require complex laboratory tests.
The object of research is the process of identifying and analyzing the course of liver cirrhosis treatment using information technologies.
The aim of research is development of information system for diagnosing and analyzing the course of liver cirrhosis treatment on the basis of mathematical simulation.
An array of observations in 412 patients was applied as the clinical material, in the presence of informative consent of patients, who were divided into two groups according to the degree of liver damage. The analysis of general information about the patients, the results of biochemical blood tests and prescribed treatment was allocated a number of informative signs used in the simulation. The correctness of the chosen informative signs confirmed literature data and high accuracy of the mathematical models. It was determined that the indicators included in the stage of developing models affect the availability of liver disease with high degrees (p <0,001).
The mathematical models estimate the probability of liver disease with high degree (including liver cirrhosis) and probability of liver disease with high degree (including liver cirrhosis) in the remote period after treatment.
The automated system developed for the practical implementation of obtained mathematical models and designed to the diagnosing and selecting an optimal treatment of liver diseases.
Validity of an automated system that implements mathematical models was confirmed by testing of the independent test sample. The total classification accuracy was 84,9 %.
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Copyright (c) 2016 Олена Костянтинівна Носовець, Олена Володимирівна Яценко, Роман Юрійович Древко
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