Abstract
The development of technology has a significant impact on every aspect of life, whether it is in the medical industry or any other profession. By making decisions based on the analysis and processing of data, artificial intelligence has demonstrated promising outcomes in the field of health care. The most crucial action is early detection of a life-threatening illness to stop its development and spread. There is a need for a technology that can be utilized to detect the virus because of how quickly it spreads. With the increased use of technology, we now have access to a wealth of COVID-19-related information that may be used to learn crucial details about the virus. In this study, we evaluated and compared various machine learning models with the traditional statistical model. The results of the study concluded the superiority of machine learning models over the statistical model. The models have depicted the percentage improvement of 0.024%, 0.103%, 0.115%, and 0.034% in accuracy, MSE, R2 score, and ROC score, respectively.
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Saggu, J., Bansal, A. (2023). Investigation of Statistical and Machine Learning Models for COVID-19 Prediction. In: Swaroop, A., Polkowski, Z., Correia, S.D., Virdee, B. (eds) Proceedings of Data Analytics and Management. ICDAM 2023. Lecture Notes in Networks and Systems, vol 788. Springer, Singapore. https://doi.org/10.1007/978-981-99-6553-3_14
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DOI: https://doi.org/10.1007/978-981-99-6553-3_14
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