Machine learning-based mortality rate prediction using optimized hyper-parameter.

Journal: Computer methods and programs in biomedicine

PMID: 32889405

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.

Authors

Y A Khan

School of Statistics, Jiangxi University of Finance and Economics, Nanchang, China; Department of Mathematics and Statistics, Hazara University, Mansehra, Pakistan.
S Z Abbas

School of Mathematics and Statistics, Beijing Institute of Technology, Beijing 100081, China; Department of Mathematics and Statistics, Hazara University, Mansehra, Pakistan. Electronic address: abbassyedzaheer@tdtu.edu.vn.
Buu-Chau Truong

Faculty of Mathematics and Statistics, Ton Duc Thang University, Ho Chi Minh City, Vietnam. Electronic address: truongbuuchau@tdtu.edu.vn.

Keywords

Computational Biology COVID-19 Humans Machine Learning Models, Biological Models, Statistical Nonlinear Dynamics Normal Distribution Pandemics Regression Analysis Statistics, Nonparametric Sweden

External Resources

View on PubMed Access via DOI PubMed (32889405)

Machine learning-based mortality rate prediction using optimized hyper-parameter.

Abstract

Authors

Keywords

External Resources

Popular Topics

Recent Journals