Predictive modeling of telemedicine implementation in central Asia using neural networks

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.