Covid-19 Comparative Data Analysis Using Various Machine Learning Regression Algorithms
Abstract
In this study, the authors investigated the potential of machine learning (ML) algorithms to forecast the spread of COVID-19 using data from three distinct datasets: confirmed, deceased, and recoverable cases. Eight different ML techniques were employed to develop regression models, and the Root Mean Squared Error (RMSE) score was used to assess their accuracy. The study found that some algorithms performed better than others for specific dataset combinations. Linear regression, ridge regression, Bayesian ridge regression, and least angle regression were the most accurate algorithms for predicting confirmed and recoverable cases, while decision tree regression and random forest regression were the best for deceased cases. The study offers valuable insights into the ability of ML to identify patterns and trends in virus transmission, enabling more precise predictions about its future course. However, ensuring that the data utilized to train machine learning models are trustworthy and accurate in reflecting the actual situation is essential. The quality and accessibility of data significantly impact the accuracy of machine learning models, particularly in the case of COVID-19, where accurate data exchange and collection have been challenging. Overall, the findings of this study contribute to the growing body of knowledge on the application of ML in healthcare and disease forecasting, potentially providing valuable information for policy-makers and healthcare professionals.
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