Реферат: After COVID-19 pandemic, the epidemilogical data prediction had become of a great importance. Since that, numerous different prognostic models, including those involving neural-network based, have been developed, applied and verified. Shortterm models are capable to reproduce the oscillacion, but incapable to make a long term prognosis; long-term ones suffer from the noise in the data and require its reduction. In this paper, we propose a method of data prediction using values range discretization as an alternative to the smoothing to get rid of noise-borne problems and applying lag prediction. It is shown that the approach is capable to improve the prognosis quality even for the irregurlar data. Keywords: epidemiology, neural network, prognosis, discretization.

Библиографическая ссылка: Elistratov S.A.
Data discretization in prognostic models for epidemiology
Cifra. Медико-биологические науки. 2024. N3. 2 :1-6. DOI: 10.60797/BMED.2024.3.2 РИНЦ

Даты:

Принята к публикации:	12 дек. 2024 г.
Опубликована в печати:	27 дек. 2024 г.
Опубликована online:	27 дек. 2024 г.

Идентификаторы БД:

РИНЦ:

77259727

Цитирование в БД: Пока нет цитирований

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