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Dietary, comorbidity, and geo-economic data fusion for explainable COVID-19 mortality prediction by Milena Trajanoska; Risto Trajanov; Tome Eftimov is a Computer Science article available to read on EtoBox.
What is Dietary, comorbidity, and geo-economic data fusion for explainable COVID-19 mortality prediction about?
Many factors significantly influence the outcomes of infectious diseases such as COVID-19. A significant focus needs to be put on dietary habits as environmental factors since it has been deemed that imbalanced diets contribute to chronic diseases. However, not enough effort has been made in order to assess these relations. So far, studies in the field have shown that comorbid conditions influence the severity of COVID-19 symptoms in infected patients. Furthermore, COVID-19 has exhibited seasonal patterns in its spread; therefore, considering weather-related factors in the analysis of the mortality rates might introduce a more relevant explanation of the disease’s progression. In this work, we provide an explainable analysis of the global risk factors for COVID-19 mortality on a national scale, considering dietary habits fused with data on past comorbidity prevalence and environmental factors such as seasonally averaged temperature geolocation, economic and development indices, undernourished and obesity rates. The innovation in this paper lies in the explainability of the obtained results and is equally essential in the data fusion methods and the broad context considered in the a
Who reads Dietary, comorbidity, and geo-economic data fusion for explainable COVID-19 mortality prediction?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Milena Trajanoska; Risto Trajanov; Tome Eftimov
- Publisher
- Elsevier BV
- Published
- 2022
- Language
- EN
- Field
- Computer Science (Physical Sciences)