African Health Sciences
Makerere University Medical School
Vol. 21, No. 1, 2021, pp. 194-206
Bioline Code: hs21038
Full paper language: English
Document type: Research Article
Document available free of charge
African Health Sciences, Vol. 21, No. 1, 2021, pp. 194-206
© Copyright 2021 - Dhamodharavadhani S et al.
COVID-19 mortality rate prediction for India using statistical neural networks and gaussian process regression model|
Dhamodharavadhani, S & Rathipriya, R
The primary purpose of this research is to identify the best COVID-19 mortality model for India using regression models
and is to estimate the future COVID-19 mortality rate for India. Specifically, Statistical Neural Networks ( Radial Basis
Function Neural Network (RBFNN), Generalized Regression Neural Network (GRNN)), and Gaussian Process Regression
(GPR) are applied to develop the COVID-19 Mortality Rate Prediction (MRP) model for India. For that purpose, there are
two types of dataset used in this study: One is COVID-19 Death cases, a Time Series Data and the other is COVID-19
Confirmed Case and Death Cases where Death case is dependent variable and the Confirmed case is an independent variable.
Hyperparameter optimization or tuning is used in these regression models, which is the process of identifying a set of
optimal hyperparameters for any learning process with minimal error. Here, sigma (σ) is a hyperparameter whose value is
used to constrain the learning process of the above models with minimum Root Mean Squared Error (RMSE). The performance
of the models is evaluated using the RMSE and 'R2 values, which shows that the GRP model performs better than
the GRNN and RBFNN.
Covid-19; India; mortality rate; mortality prediction; regression model; hyperparameter tuning; GPR; GRNN; RBFNN.