The COVID-19 pandemic has wreaked havoc across the globe. As of July 30, 2021, nearly 197.5 million people have been infected with coronavirus, and 4.2 million people have died. The COVID-19 pandemic has resulted in significant pressure on healthcare systems around the globe. The need for effective diagnostic, prognostic and therapeutic procedures has never been as urgent as it is today. Despite significant investment and research on understanding and managing this disease, there is still a lack of efficient predictive models for patient stratification and management of this disease. 

Since the transmission rate of COVID-19 is extremely high, healthcare facilities are continuously facing the challenges of managing patient surges while ensuring the safety of staff, family members, and patients suffering from other illnesses. 

Machine learning techniques and artificial intelligence have been deployed to compute the risk of infection and to perform effective survival analysis and classification. However, so far, the results from these models have neither been that accurate nor consistent. This does not mean that AI and ML cannot be used more accurately. AI is and will remain a useful tool for the computing risk factor, classification, drug analysis, and response to disease, but its correct use and application is critical for optimum benefits. This is especially true in the case of a new disease like COVID-19 because understanding the disease and its impact on infected patients is essential for clinicians for improved patient outcomes. Hence, analytical models that can help predict the probability of survival and also highlight the impact of symptoms on survival probability and other related features and characteristics of the disease can be extremely useful and can provide valuable information to scientists, researchers, and healthcare providers.  

Already, this need for more data and information has been addressed by scientists and clinicians, and thousands of articles have been published on this topic since the beginning of the pandemic. The possible role of machine learning and artificial intelligence in providing useful insights into the pandemic through multivariable prediction models cannot be denied. However, this is not an easy task because the incorporation of machine learning in biomedical classification analysis requires statistical and coding knowledge, expertise in the use of algorithms, selecting the right features, designing accurate…

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