Machine Learning Based Prediction Models for the Percentage Deaths Due to COVID-19
14th International Conference on Developments in eSystems Engineering, DeSE 2021
; 2021-December:418-423, 2021.
Article
in English
| Scopus | ID: covidwho-1769570
ABSTRACT
Worldwide COVID-19 pandemic is currently affecting all countries and led to loss of human life. A lot of scientific research are conducted in different areas to improve the future response. The purpose of the project is to use Machine learning (ML) techniques in predicting COVID-19 deaths which will enhance the hospitals response. This paper contributes by developing models that can predict COVID-19 deaths based on three factors total number of elderly patients (greater than 65 years), diabetic patients, and smoking patients. Gaussian Process Regression (GPR), Support Vector Regression (SVR), Artificial Neural Network-Multi Layer Perceptron (ANN-MLP), and Artificial Neural Network-nonlinear autoregressive network with exogenous inputs (ANN-NARX) approaches are used to build the predictive models. All models are trained and tested using trusted data reported by the World Health Organization (WHO) in various countries. The developed models revealed very good results with excellent prediction rate and performance, especially GPR, which has the best performance. Also, it showed that region-based predictive models are more suitable than a single general model. The GPR predictive model showed the best performance compared to other models. © 2021 IEEE.
Artificial neural network; COVID-19; Gaussian process regression; prediction model; support vector regression; Forecasting; Gaussian distribution; Gaussian noise (electronic); Machine learning; Network layers; Regression analysis; Future response; Human lives; Machine-learning; Performance; Prediction modelling; Predictive models; Scientific researches; Support vector regressions; Multilayer neural networks
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
14th International Conference on Developments in eSystems Engineering, DeSE 2021
Year:
2021
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS