Machine learning-based mortality rate prediction using optimized hyper-parameter. (December 2020) - Khan, Y.A.; Abbas, S.Z.; Truong, Buu-Chau
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 for the COVID-19 death rate in Pakistan. Hence, the best-fit is the Sweden model to control the mortality rate.