Machine learning-based mortality rate prediction using optimized hyper-parameter. (December 2020)
- Record Type:
- Journal Article
- Title:
- Machine learning-based mortality rate prediction using optimized hyper-parameter. (December 2020)
- Main Title:
- Machine learning-based mortality rate prediction using optimized hyper-parameter
- Authors:
- Khan, Y.A.
Abbas, S.Z.
Truong, Buu-Chau - Abstract:
- Highlights: Death, mortality rate Mortality prediction Regression model Hyperparameter tuning Parameter optimization Abstract: Objective and background: The current scenario of the Pandemic of COVID-19 demands multi-channel investigations and predictions. A variety of prediction models are available in the literature. The majority of these models are based on extrapolating by the parameters related to the diseases, which are history-oriented. Instead, the current research is designed to predict the mortality rate of COVID-19 by Regression techniques in comparison to the models followed by five countries. Methods: The Regression method with an optimized hyper-parameter is used to develop these models under training data by Machine Learning Technique. Results: The validity of the proposed model is endorsed by considering the case study on the data for Pakistan. Five distinct models for mortality rate prediction are built using Confirmed cases data as a predictor variable for France, Spain, Turkey, Sweden, and Pakistan, respectively. The results evidenced that Sweden has a fewer death case over 20, 000 confirmed cases without observing lockdown. Hence, by following the strategy adopted by Sweden, the chosen entity will control the death rate despite the increase of the confirmed cases. Conclusion: The evaluated results notice the high mortality rate and low RMSE for Pakistan by the GPR method based Mortality model. Therefore, the morality rate based MRP model is selected forHighlights: Death, mortality rate Mortality prediction Regression model Hyperparameter tuning Parameter optimization Abstract: Objective and background: The current scenario of the Pandemic of COVID-19 demands multi-channel investigations and predictions. A variety of prediction models are available in the literature. The majority of these models are based on extrapolating by the parameters related to the diseases, which are history-oriented. Instead, the current research is designed to predict the mortality rate of COVID-19 by Regression techniques in comparison to the models followed by five countries. Methods: The Regression method with an optimized hyper-parameter is used to develop these models under training data by Machine Learning Technique. Results: The validity of the proposed model is endorsed by considering the case study on the data for Pakistan. Five distinct models for mortality rate prediction are built using Confirmed cases data as a predictor variable for France, Spain, Turkey, Sweden, and Pakistan, respectively. The results evidenced that Sweden has a fewer death case over 20, 000 confirmed cases without observing lockdown. Hence, by following the strategy adopted by Sweden, the chosen entity will control the death rate despite the increase of the confirmed cases. Conclusion: The evaluated results notice the high mortality rate and low RMSE for Pakistan by the GPR method based Mortality model. Therefore, the morality rate based MRP model is selected for the COVID-19 death rate in Pakistan. Hence, the best-fit is the Sweden model to control the mortality rate. … (more)
- Is Part Of:
- Computer methods and programs in biomedicine. Volume 197(2020)
- Journal:
- Computer methods and programs in biomedicine
- Issue:
- Volume 197(2020)
- Issue Display:
- Volume 197, Issue 2020 (2020)
- Year:
- 2020
- Volume:
- 197
- Issue:
- 2020
- Issue Sort Value:
- 2020-0197-2020-0000
- Page Start:
- Page End:
- Publication Date:
- 2020-12
- Subjects:
- Prediction -- Mortality rate -- Hyper-parameter -- Optimization -- Covid-19 deaths rate
Medicine -- Computer programs -- Periodicals
Biology -- Computer programs -- Periodicals
Computers -- Periodicals
Medicine -- Periodicals
Médecine -- Logiciels -- Périodiques
Biologie -- Logiciels -- Périodiques
Biology -- Computer programs
Medicine -- Computer programs
Periodicals
Electronic journals
610.28 - Journal URLs:
- http://www.sciencedirect.com/science/journal/01692607 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.cmpb.2020.105704 ↗
- Languages:
- English
- ISSNs:
- 0169-2607
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3394.095000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 14946.xml