NEURAL NETWORK PREDICTION OF THE FREQUENCY OF ADMISSION OF PATIENTS WITH CARDIOVASCULAR DISEASES TO CLINICAL INSTITUTIONS

Authors

  • Giyos Pulatov

    “TIIAME” National Research University, Tashkent, Uzbekistan

    Author

  • Gulxayo Pulatova

    Ferghana State Technical University

    Author

Keywords:
Forecasting, time series, meteorological factors, heliogeophysical factors, cardiovascular diseases, LSTM neural network, neural network architecture, neural network training.
Abstract

The issues of using recurrent neural networks with long short-term memory (LSTM) in predicting the admission of patients with cardiovascular diseases are considered. The forecast of the frequency of admission of patients with cardiovascular diseases (CVD) to medical institutions is based on meteorological (ambient temperature, atmospheric pressure, air humidity and wind speed) and heliogeophysical factors (geomagnetic activity index). The stages of architecture selection and training of LSTM neural networks are described in detail. The results of predicting the frequency of admission of patients with CVD to medical institutions in two cities of the Republic of Uzbekistan (Nukus, Samarkand) using the recurrent deep learning neural network LSTM implemented using the Keras library in the Python software environment are presented.

References

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Published
2026-03-24
Issue
Vol. 2 No. 3 (2026)
Section
Articles
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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

NEURAL NETWORK PREDICTION OF THE FREQUENCY OF ADMISSION OF PATIENTS WITH CARDIOVASCULAR DISEASES TO CLINICAL INSTITUTIONS. (2026). Eureka Journal of Artificial Intelligence and Data Innovation, 2(3), 46-54. https://eurekaoa.com/index.php/11/article/view/642

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