OBJECTIVE: Corona virus-related disease, a deadly illness, has raised public health issues worldwide . The majority of individuals infected are multiplying . The government takes aggressive steps to quarantine people, people exposed to infection, and clinical trials for treatment . Subsequently recommends critical care for the aged, children, and health-care personnel . While machine learning methods have been previously used to augment clinical decisions, there is now a demand for``Emergency ML ."With rapidly growing datasets, there also remain important considerations when developing and validating ML models .
METHODS: This paper reviews the recent study that applies machine-learning technology addressing Corona virus-related disease issues' challenges in different perspectives . The report also discusses various treatment trials and procedures on Corona virus-related disease infected patients providing insights to physicians and the public on the current treatment challenges .
RESULTS: The paper provides the individual with insights into certain precautions to prevent and control the spread of this deadly disease .
CONCLUSION: This review highlights the utility of evidence-based machine learning prediction tools in several clinical settings, and how similar models can be deployed during the Corona virus-related disease pandemic to guide hospital frontlines and health-care administrators to make informed decisions about patient care and managing hospital volume . Further, the clinical trials conducted so far for infected patients with Corona virus-related disease addresses their results to improve community alertness from the viewpoint of a well-known saying,``prevention is always better . ''