Abstract
COVID-19 is a viral infectious disease that originated from Hubei Province being Wuhan as the central outbreak point. This paper proposes a model where the probability of getting infected will be derived from the person’s symptoms. The prediction is very much required to understand the interdependencies of the category of symptoms responsible for the infection. For this work, we used various algorithms for the classification like logistic regression, naïve Bayes, random forest, linear support vector classifier, and decision tree. The performance metrics of various algorithms were compared, and the successful method was discussed. The approximate mean accuracy score using these algorithms was found to be 78%.
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Banik, S., Banik, S., Ghosh, A., Mukherjee, A. (2021). Probabilistic Estimation of COVID-19 Using Patient’s Symptoms. In: Singh, T.P., Tomar, R., Choudhury, T., Perumal, T., Mahdi, H.F. (eds) Data Driven Approach Towards Disruptive Technologies. Studies in Autonomic, Data-driven and Industrial Computing. Springer, Singapore. https://doi.org/10.1007/978-981-15-9873-9_29
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DOI: https://doi.org/10.1007/978-981-15-9873-9_29
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