Comparison of machine learning algorithms with regression analysis to predict the COVID-19 outbreak in Thailand

Abstract

Coronavirus disease (COVID-19) is a public health problem in Thailand. Currently, there are more than 5 million infected people and the rate has been increasing at some point. It is therefore important to forecast the number of new cases over a short period of time to assist in strategic planning for the response to COVID-19. The purpose of this research paper was to compare the efficiency and prediction of the number of COVID-19 cases in Thailand using machine learning of 8 models using a regression analysis method. Using the 475-day dataset of COVID-19 cases in Thailand, the results showed that the predictive accuracy model (R2 score) from the testing dataset was the random forest (RF) model, which was 99.06%, followed by K-nearest neighbor (KNN), XGBoost. And the decision tree (DT) had the precision of 98.97, 98.67, and 98.64, respectively. And the results of the comparison of the number of infected people obtained from the prediction The models that predicted the number of real infections were the decision tree, random forest, and XGBoost, which were effective at predicting the number of infections correctly in the 2-4 day period.

Predictive maintenance framework for assessing health state of centrifugal pumps

“…Overall results confirmed the efficiency of the proposed framework following feature selection and XGBoost algorithm implementation in industrial applications. Furthermore, authors' findings aligned with previous research where XGBoost was effectively applied in socioeconomical aspects namely medicine [33], [34], economy [35], cybersecurity [36], language processing [37] and environmental applications [38]. Regarding medical applications, feature selection and XGBoost was considered the most effective solution for heart disease classification with 99.6% accuracy [39] improving the solution of [40] where the proposed decision trees provided 97.75% accuracy.…”