Abstract:
The rapid development of digital technologies has transformed healthcare systems around the world, and
telemedicine has become the primary solution to problems related to the availability and quality of medical
care. This study examines the adoption of telemedicine in five Central Asian countries - Kazakhstan,
Kyrgyzstan, Uzbekistan, Tajikistan, and Turkmenistan - by modeling the relationship between key medical,
demographic, and technological factors and the number of telemedicine users. To identify the factors that
contribute to telemedicine adoption, a dataset of epidemiological, demographic, and digital infrastructure
indicators was analyzed. For the analysis, data from the National Statistical Office of the Republic of
Kazakhstan (2014-2024) were used. To predict the number of telemedicine users, an artificial neural
network (ANN) was used, which has a shallow network structure with four input neurons representing the
main predictors and one output neuron for potential telemedicine users. The predictive model showed
excellent accuracy, as evidenced by a very strong correlation between predicted and observed values (R =
0.99245). In addition, the reliability of the model is confirmed by its low error rates, with a mean squared
error (MSE) of 0.007 and a root mean squared error (RMSE) of 0.0839. These findings underscore the
transformative potential of telemedicine to address health challenges in Central Asia, while providing
valuable insights into the epidemiological, demographic, and technological drivers that can guide targeted
policy initiatives and strategic investments in digital infrastructure.
