Issue 1 (203), article 5


Cybernetics and Computer Engineering, 2021, 1(203)

Researcher, the Medical Information Systems Department
ORCID: 0000-0002-4407-5990

KOZAK L.M., DSc (Biology), Senior Researcher,
Leading Researcher, the Medical Information Systems Department
ORCID: 0000-0002-7412-3041

International Research and Training Center for Information Technologies
and Systems of the National Academy of Sciences of Ukraine
and Ministry of Education and Science of Ukraine,
40, Acad. Glushkov av., Kyiv, 03187, Ukraine


Introduction. The digital technologies implementation provides registration of large amounts of bio-medical data (ECG, EEG, electronic medical records) as a basis for assessing and predicting the patients` condition. Data Mining methods allow to identify the most informative indicators and typological groups, to classify the person` functional state and the patients` disease stages to predict their changes.

The purpose of the paper is to develop information technology for the classification of human health states using a set of Data Mining methods and to carry out its validation on examples of an operators` functional state and patient’s disease severity.

Results. The developed IT unites several stages: I — data pre-processing; II — clustering, selecting the homogeneous groups (data segmentation); III — predictors` identification; IV — classifying the studied states, development of predictive models using machine learning algorithms (Decision trees, Support vector machines, neural networks) and the method cross-validation. The proposed IT was used to classify the operators` functional statе and the patients` severity in case of disease progression.

Conclusions. The IT use to assess the operators` activity successes made it possible to identify the most informative HRV indicators, changes in which can predict the operators` reliability, taking into account the type of vegetative regulation. Assessing the disease activity of children with dysplasia with IT use made it possible to identify diagnostic markers of CCC and develop diagnostic rules for determining the stages of the disease by ECG parameters (T wave symmetry, an integral indicator of the ST_T segment shape).

Keywords: information technology, Data Mining, machine learning models, severity of the patient.

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Received 31.11.2020