COMPARATIVE ANALYSIS OF THE RESULTS OF THE APPLICATION OF DIFFERENT NEURAL NETWORK ARCHITECTURES FOR THE PREDICTION OF INFECTIOUS DISEASES
DOI:
https://doi.org/10.34132/mspc2025.01.14.11Keywords:
Forecasting, time series, machine learning methods, neural networks.Abstract
This paper presents the results of time series forecasting using machine learning methods and mobile technologies. The main results of this work are a comparison of the effectiveness of different neural network architectures in predicting the spread of infectious diseases in Ukraine for the period from December 2016 to January 2024. To achieve the goal, the following tasks were solved: the current state of the time series forecasting was analyzed; existing analogs of the systems were analyzed; the necessary neural network architectures were selected as one of the machine learning methods; a dataset for infectious diseases spread was analyzed and normalized; conducted testing. The developed system can be an effective tool for rational management decisions to ensure the epidemiological well-being and biosecurity of the population.
References
Goniewicz, K., Burkle, F.M., Horne, S., Borowska-Stefańska, M., Wiśniewski, S., Khorram-Manesh, A. (2021) The Influence of War and Conflict on Infectious Disease: A Rapid Review of Historical Lessons We Have Yet to Learn. Sustainability, 13, 10783. Retrieved from: https://doi.org/10.3390/su131910783.
Public Health Center of the Ministry of Health of Ukraine (2024) Infectious disease in the population of Ukraine, Retrieved from: https://phc.org.ua/kontrol-zakhvoryuvan/inshi-infekciyni-zakhvoryuvannya/infekciyna-zakhvoryuvanist-naselennya-ukraini.
Vaswani A, Shazeer N., Parmar N., Uszkoreit J., Jones L., Gomez A. N., Kaiser L., Polosukhin I., (2017) Attention Is All You Need, Advances in Neural Information Processing Systems, 2023, 3-4,. Retrieved from: https://arxiv.org/abs/1706.03762
Myttenaere, Golden, B., Grand, B. Le., Rossi, F. (2016) Mean absolute percentage error for regression models, Neurocomputing. Retrieved from: https://arxiv.org/abs/1605.02541.
