Author: Choudary, M. N. S.; Bommineni, V. B.; Tarun, G.; Reddy, G. P.; Gopakumar, G.
Title: Predicting Covid-19 Positive Cases and Analysis on the Relevance of Features using SHAP (SHapley Additive exPlanation) Cord-id: okj91isi Document date: 2021_1_1
ID: okj91isi
Snippet: COVID-19 and related infections are on the surge around the world, posing new threats to our society. There is a clear motivation to implement protective measures that aid in the effective control of future outbreaks or pandemics. The effect of the COVID-19 pandemic has prompted a flood of studies aimed at deeper understanding, monitoring, and also controlling the disease. Machine learning is increasingly becoming more prevalent in the area of medical diagnosis. With this paper, we will classify
Document: COVID-19 and related infections are on the surge around the world, posing new threats to our society. There is a clear motivation to implement protective measures that aid in the effective control of future outbreaks or pandemics. The effect of the COVID-19 pandemic has prompted a flood of studies aimed at deeper understanding, monitoring, and also controlling the disease. Machine learning is increasingly becoming more prevalent in the area of medical diagnosis. With this paper, we will classify whether the patient is affected with covid or not and elucidate the significance of every attribute on the output using SHAP (SHapley Additive exPlanation). © 2021 IEEE.
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