A COVID-19 forecasting system for hospital needs using ANFIS and LSTM models: A graphical user interface unit. (March 2022)

• Record Type: Journal Article
• Authors: Shafiekhani, Sajad; Namdar, Peyman; Rafiei, Sima
• Is Part Of: Digital health. Volume 8(2022)
• Publication Date: 2022-03
• Subjects: Coronavirus disease 2019 (COVID-19) -- long short-term memory (LSTM) network -- adaptive neuro-fuzzy inference system (ANFIS) -- demand forecasting -- hospitalization -- intensive care unit (ICU); Medical care -- Data processing -- Periodicals; Medical informatics -- Periodicals; 362.10285
• DOI: 10.1177/20552076221085057
• Languages: English
• ISSNs: 2055-2076

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 system (ANFIS), support vector regression (SVR) and decision tree model training, whilst the remaining data for the last 60 days (test data set) were used for model validation. To predict the number of ICU and non-ICU patients, we used these models. Finally, a user-friendly graphical user interface unit was designed to load any time series data (here the trend of population of COVID-19 patients) and train LSTM, ANFIS, SVR or tree models for the prediction of COVID-19 cases for one week ahead. Results: All models predicted the dynamics of COVID-19 cases in ICU and non- wards. The values of root-mean-square error and R 2 as model assessment metrics showed that ANFIS model had better predictive power among all models. Conclusion: Artificial intelligence-based forecasting models such as ANFIS system or deep learning approach based on LSTM or regression models including SVR or tree regression play a key role in forecasting the required number of beds or other types of medical facilities during the coronavirus pandemic. Thus, the designed graphical user interface of the present study can be used for optimum management of resources by health care systems amid COVID-19 pandemic.