Abstract
The outbreak of coronavirus worldwide infected people much and affected their lives and economy very badly. There are several anticipation techniques such as maintaining distance, health hygiene, and refrain congregation. The situation also led various researchers to discover remedies to overcome these situations using machine learning algorithms. This paper provides an early and necessarily selective review, discussing the contribution made by machine learning to fight against deadly disease, COVID-19, and it is grouped into drug discovery, trend analysis, medical image analysis, and other machine learning techniques. Correlation analysis between attributes like gender, age, and death rate is also performed using popular correlation tests such as Pearson, Spearman rank, and Kendall rank correlation test to reveal the relationships between the attributes. This paper also focuses on available amenities for providing the treatment for the patients such as the number of primary health centers, community health centers, sub-district hospitals, district hospitals, beds available in those centers. The results show that age is positively correlated with the deceased rate irrespective of the algorithm. This paper also focuses on the impending research directions in COVID-19.
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khilar, R., Subetha, T., Mohanty, M.N. (2022). COVID-19 Severıty Predıctıons: An Analysis Usıng Correlatıon Measures. In: Dehuri, S., Prasad Mishra, B.S., Mallick, P.K., Cho, SB. (eds) Biologically Inspired Techniques in Many Criteria Decision Making. Smart Innovation, Systems and Technologies, vol 271. Springer, Singapore. https://doi.org/10.1007/978-981-16-8739-6_4
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