Performance evaluation of regression models for COVID-19: A statistical and predictive perspective

Ain Shams Engineering Journal

Rijwan Khan¹  Aditi Srivastava²  Fahad Algarni²  Mohammad Ayoub Khan³  Indrajeet Kumar³  Akshika Choudhary⁴ 

[1] Corresponding author at: College of Computing and Information Technology, University of Bisha, Bisha 67714, Saudi Arabia.;ABES Institute of Technology, Ghaziabad, U.P., India;College of Computing and Information Technology, University of Bisha, Bisha 67714, Saudi Arabia;Department of Computer Science and Engineering, Graphic Era Hill University, Dehradun, India;

关键词: COVID-19;    Machine learning;    Regression;    Prediction;    Non-linear;   

DOI  :  

来源: DOAJ

【 摘 要 】

Research is very important in the pandemic situation of COVID-19 to deliver a speedy solution to this problem. COVID-19 has presented governments, corporations and ordinary citizens around the world with technology playing an essential role to tackle the crisis. Moderate and flexible innovation arrangements that can speed up progress towards giving critical well-being ability are proved hourly. Knowledge with the aid of creativity must be obtained, accepted and analysed in a short time frame. In this example, the machine learning model has a major role to play in predicting the number of next positive COVID-19 cases to come. For government departments to take effective and strengthened future COVID-19 planning and innovation. The ongoing global pandemic of COVID-19 has been non-linear and dynamic. Due to the especially perplexing nature of the COVID-19 episode and its diversity from country to country, this study recommends machine learning as a convincing means to demonstrate flare-up. In this linear regression, polynomial regression, ridge regression, polynomial ridgeregression, support vector regression models, the COVID-19 data set from multiple on-line tools have been evaluated. During the work process comprehensive experiments were performed and each test was evaluated with the parameters mean square error (MSE), medium absolute error (MAE), root mean square error (RMSE) and R2 score. This study also offers a path for future research using regression models based on machine learning. Precise validation and data analysis can contribute to strategies for healing and disease prevention at an early stage. A systematic comprehensive strategy is a new philosophy in which statistical data for government agencies and community can be forecast.

【 授权许可】

Unknown