eISSN: 2449-8580
ISSN: 1734-3402
Family Medicine & Primary Care Review
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2/2018
vol. 20
 
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abstract:
Original paper

On an algorithm for decision-making for the optimization of disease prediction at the primary health care level using neural network clustering

Petro Selskyy, Dmytro Vakulenko, Anatolii Televiak, Taras Veresiuk

Family Medicine & Primary Care Review 2018; 20(2): 171–175
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Background
A number of studies have been aimed at solving the problems of introducing information technology and systems, but the questions of informatization in rural medicine are not completely resolved. It is important to optimize the prognosis of diseases using available and inexpensive information methods for the improvement of primary healthcare.

Objectives
The aim of our study was to develop an algorithm to optimize the decision-making prognosis of disease at the primary health care level based on information methods.

Material and methods
The data used for analysis originated from the survey results of 63 patients with hypertension in educational and practical centers of primary health care (EPCPHC) of Ternopil region (Ukraine). For a deeper analysis and clustering, the neural network approach was used with the NeuroXL Classifier add-in application for Microsoft Excel.

Results
Thirteen (19.40%) patients experienced health deterioration and the development of complications. It has been established that neural network clustering could effectively and objectively allocate patients to the appropriate category in terms of the average survey results. Cluster analysis results have shown that the combination of high blood pressure (systolic, diastolic and pulse) gave reason to anticipate the deterioration of patients’ conditions.

Conclusions
A decision algorithm was created in order to optimize the prediction of diseases at the primary health care level, and also to correct examination and treatment based on an analysis of average values of patients’ examination and the use of neural network clustering.

keywords:

primary health care, hypertension, algorithms, neural networks (computer), cluster analysis

 
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