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Predicting Covid-19 Positive Cases and Analysis on the Relevance of Features using SHAP (SHapley Additive exPlanation)

Publication Type : Conference Paper

Publisher : Proceedings of the 2nd International Conference on Electronics and Sustainable Communication Systems

Source : Proceedings of the 2nd International Conference on Electronics and Sustainable Communication Systems, ICESC 2021, 2021, pp. 1892–1896

Campus : Amritapuri

School : School of Engineering

Center : Computer Vision and Robotics, Research & Projects

Department : Computer Science

Verified : Yes

Year : 2021

Abstract : 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).

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