Abstract
COVID-19 formally termed as “2019 novel coronavirus,” is disrupting socioeconomic conditions throughout the world. Due to the unavailability of efficient ways to predict the severity level of COVID-19, governmental officials and policymakers of different countries are facing difficulties to take precautionary measures for minimizing risks. This paper presents a model trained to predict COVID-19 situation severity level using XGBoost which is a gradient boosting algorithm. To categorize severity level, SIR epidemiological method has been employed which can express the current condition of any area affected by contagious diseases like COVID-19, analyzing the number of susceptible people stands for S, the number of infected people stands for I, and the number of recovered people stands for R. By comparing the evaluation metrics of the selected machine learning model with other machine learning algorithms, it is deduced that the model performs better for less training time(speed), gives an accuracy rate of 95%, and has the ability to reduce over-fitting.
These authors contributed equally to this work.
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Notes
- 1.
The Dataset is downloaded from: https://www.kaggle.com/vignesh1694/Covid-19-coronavirus.
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Tahsin, L., Roy, S. (2022). Prediction of COVID-19 Severity Level Using XGBoost Algorithm: A Machine Learning Approach Based on SIR Epidemiological Model. In: Reddy, V.S., Prasad, V.K., Mallikarjuna Rao, D.N., Satapathy, S.C. (eds) Intelligent Systems and Sustainable Computing. Smart Innovation, Systems and Technologies, vol 289. Springer, Singapore. https://doi.org/10.1007/978-981-19-0011-2_7
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