Performance Evaluation of Regression Models for the Prediction of the Covid-19 Reproduction Rate

Bibliographic Data

ID22070494
AuthorsJayakumar Kaliappan (0000-0002-6044-6667, Vellore Institute of Technology University), Kathiravan Srinivasan (0000-0002-9352-0237, Vellore Institute of Technology University), Saeed Mian Qaisar (0000-0002-4268-3482, Effat University), Karpagam Sundararajan (0000-0003-2094-9072, Vellore Institute of Technology University), Chuan-Yu Chang (0000-0001-9476-8130, National Yunlin University of Science and Technology, corresponding author), Suganthan C, C Suganthan (Vellore Institute of Technology University)
Year2021
Volume9
Pages729795-729795
Publication date2021-09-14
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2021.729795
PMID34595149
OpenAlexW3200921446
LanguageEN
Citations received2
References cited35

This paper aims to evaluate the performance of multiple non-linear regression techniques, such as support-vector regression (SVR), k-nearest neighbor (KNN), Random Forest Regressor, Gradient Boosting, and XGBOOST for COVID-19 reproduction rate prediction and to study the impact of feature selection algorithms and hyperparameter tuning on prediction. Sixteen features (for example, Total_cases_per_million and Total_deaths_per_million) related to significant factors, such as testing, death, positivity rate, active cases, stringency index, and population density are considered for the COVID-19 reproduction rate prediction. These 16 features are ranked using Random Forest, Gradient Boosting, and XGBOOST feature selection algorithms. Seven features are selected from the 16 features according to the ranks assigned by most of the above mentioned feature-selection algorithms. Predictions by historical statistical models are based solely on the predicted feature and the assumption that future instances resemble past occurrences. However, techniques, such as Random Forest, XGBOOST, Gradient Boosting, KNN, and SVR considered the influence of other significant features for predicting the result. The performance of reproduction rate prediction is measured by mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), R-Squared, relative absolute error (RAE), and root relative squared error (RRSE) metrics. The performances of algorithms with and without feature selection are similar, but a remarkable difference is seen with hyperparameter tuning. The results suggest that the reproduction rate is highly dependent on many features, and the prediction should not be based solely upon past values. In the case without hyperparameter tuning, the minimum value of RAE is 0.117315935 with feature selection and 0.0968989 without feature selection, respectively. The KNN attains a low MAE value of 0.0008 and performs well without feature selection and with hyperparameter tuning. The results show that predictions performed using all features and hyperparameter tuning is more accurate than predictions performed using selected features

Feature selection · Gradient boosting · Hyperparameter · Linear regression · Mean squared error · Random forest · Regression · Statistics · Support vector machine · Word error rate · Computer Science · COVID-19 diagnosis using AI · COVID-19 epidemiological studies · COVID-19 Pandemic Impacts · Mathematics · Artificial Intelligence

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Open DOIOpen Access