Neural Networks in Intelligent Analysis Medical Data for
Decision Support
Vasyl Sheketa
a
, Mykola Pasieka
a
, Nelly Lysenko
b
, Oleksandra Lysenko
b
, Nadia Pasieka
b
and
Yulia Romanyshyn
a
a
National Tech. University of Oil & Gas, Ivano-Frankivsk, 76068, Ukraine
b
Vasyl Stefanyk Precarpathian National University, Ivano-Frankivsk, 76000, Ukraine,
Abstract
The main purpose of the work was to consider the problem of neural networks and their
application, especially for data management and control in the medical industry. The software
product, analyzes processing of unstructured and poorly structured medical data reliability, to
support decision-making, implements the neural network, was developed and studied from sets
of user-defined information flows. On the basis of the scientific task, the program training
algorithm was developed, which provides comprehensive support for decision-making based
on the study. The developed software application is focused on cross-platform, and the
graphical interface is implemented using Java FX. The software product provides a network
for the reverse propagation of neural network errors (BackPropagation) and a network of
directed random search (Directed Random Search). Designed neural network is trained and
further recognizes the type of distribution (uniform, normal) on the specified characteristics,
and used the rule "3 Sigma" to generate synthetic data. According to the study, we can conclude
that the Directed Random Search learning algorithm, although more complex to implement the
search for relevant medical documents, works much faster than the classical reverse
distribution.
Keywords 1
Neural network, mathematical models, systems architecture, software applications, CEUR-WS
Introduction
1.1. Basic concepts of neural networks
Artificial neural networks – mathematical models, as well as their software and hardware
implementation, built on the principle of biological neural networks - networks of nerve cells of a living
organism. Systems, architecture and principle of operation are based on analogy with the brain of living
beings. The key element of these systems is an artificial neuron as an imitation model of the brain nerve
cell, a biological neuron. This term arose when studying the processes occurring in the brain and
attempting to simulate these processes. The first such attempt was the McCalock and Pitts neural
networks [1, 4, 6, 11, 12, 18, 23, 27, 34, 37]. As a consequence, after the development of training
algorithms, the obtained models were used for practical purposes: in forecasting tasks, for pattern
recognition, in control tasks, and others [21]. The neural networks can be classified by:
Type of input information:
analog neural networks (use information in the form of real numbers);
binary neural networks (operate with information presented in binary form).
Character of learning:
IDDM’2020: 3rd International Conference on Informatics & Data-Driven Medicine, November 19–21, 2020, Växjö, Sweden
MAIL: vasylsheketa@gmail.com (A. 1); pms.mykola@gmail.com (A. 2); lysenkowa@gmail.com (A. 3); leuro@list.ru (A. 4);
pasyekanm@gmail.com (A. 5); yulromanyshyn@gmail.com (A. 6)
ORCID: 0000-0002-1318-4895 (A. 1); 0000-0002-3058-6650 (A. 2); 0000-0002-1029-7843 (A. 3); 0000-0002-1029-7843 (A. 4);
0000-0002-4824-2370 (A. 5); 0000-0001-7231-8040 (A. 6)
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Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)