A COVID-19 forecasting system for hospital needs using ANFIS and LSTM models: A graphical user interface unit.

ABSTRACT:

Background

Centers for Disease Control and Prevention data showed that about 40% of coronavirus disease 2019 (COVID-19) patients had been suffering from at least one underlying medical condition were hospitalized; in which nearly 33% of them needed to be admitted to the intensive care unit (ICU) to receive specialized medical services. Our study aimed to find a proper machine learning algorithm that can predict confirmed COVID-19 hospital admissions with high accuracy.

Methods

We obtained data on daily COVID-19 cases in regular medical inpatient units, emergency department, and ICU in the time window between 21 July 2020 and 21 November 2021. Data for the first 183 days (training data set) were used for long short-term memory (LSTM) network, adaptive neuro-fuzzy inference sy

SUBMITTER: Shafiekhani S 

PROVIDER: S-EPMC8961204 | biostudies-literature | 2022 Jan-Dec

REPOSITORIES: biostudies-literature