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Identifying Factors in COVID-19 AI Case Predictions
Int. Conf. Soft Comput. Mach. Intell., ISCMI ; : 192-196, 2020.
Article in English | Scopus | ID: covidwho-1075739
ABSTRACT
Many machine learning methods are being developed to predict the spread of COVID-19. This paper focuses on the expansion of inputs that may be considered in these models. A correlation matrix is used to identify those variables with the highest correlation to COVID-19 cases. These variables are then used and compared in three methods that predict future cases a Support Vector Machine Regression (SVR), Multidimensional Regression with Interactions, and the Stepwise Regression method. All three methods predict a rise in cases similar to the actual rise in cases, and importantly, are all able to predict to a certain degree the unexpected dip in cases on the 10th and 11th day of prediction. © 2020 IEEE.

Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Prognostic study Language: English Journal: Int. Conf. Soft Comput. Mach. Intell., ISCMI Year: 2020 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Prognostic study Language: English Journal: Int. Conf. Soft Comput. Mach. Intell., ISCMI Year: 2020 Document Type: Article