Predictive Model of Lyme Disease Epidemic Process Using Machine Learning Approach

نویسندگان

چکیده

Lyme disease is the most prevalent tick-borne in Eastern Europe. This study focuses on development of a machine learning model based neural network for predicting dynamics epidemic process. A retrospective analysis cases reported Kharkiv region, East Ukraine, between 2010 and 2017 was performed. To develop process, multilayered used, backpropagation algorithm or generalized delta rule used its learning. The adequacy constructed forecast tested real statistical data incidence disease. took 22.14 s, mean absolute percentage error 3.79%. software package prediction basis has been developed. Results simulation have shown an unstable epidemiological situation disease, which requires preventive measures at both population level individual protection. Forecasting particular importance conditions hostilities that are currently taking place including endemic territories.

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ژورنال

عنوان ژورنال: Applied sciences

سال: 2022

ISSN: ['2076-3417']

DOI: https://doi.org/10.3390/app12094282