Predicting mortality rate and associated risks in COVID-19 patients

Satpathy, Suneeta; Mangla, Monika; Sharma, Nonita; Deshmukh, Hardik; Mohanty, Sachinandan

The genesis of novel coronavirus (COVID-19) was from Wuhan city, China in December 2019, which was later declared as a global pandemic in view of its exponential rise and spread around the world. Resultantly, the scientific and medical research communities around the globe geared up to curb its spread. In this manuscript, authors claim competence of AI-mediated methods to predict mortality rate. Efficient prediction model enables healthcare professionals to be well prepared to handle this unpredictable situation. The prime focus of the study is to investigate efficient prediction model. In order to determine the most effective prediction model, authors perform comparative analysis of numerous models. The performance of various prediction models is compared using various error metrics viz. Root mean square error, mean absolute error, mean square error and [Formula: see text] . During comparative analysis, Auto seasonal auto regressive integrated moving average model proves its competence over comparative models.