SARS-CoV-2 Mortality Risk Prediction Using Machine Learning Algorithms to Aid Medical Decision-Making

The coronavirus has affected the world in every possible aspect such as loss of economy, infrastructure, and moreover human life. In the era of growing technology, artificial intelligence and machine learning can help find a way in reducing mortality so, we have developed a model which predict the mortality risk in patients infected by COVID-19. We used the dataset of 146 countries which consists of laboratory samples of COVID-19 cases. This study presents a model which will assist hospitals in determining who must be given priority for treatment when the system is overburdened. As a result, the accuracy of the mortality rate prediction demonstrated is 91.26%. We evaluated machine learning algorithms namely decision tree, support vector machine, random forest, logistic regression, and K-nearest neighbor for prediction. In this study, the most relevant features and alarming symptoms were identified. To evaluate the results, different performance measures were used on the model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Parameter Based Literature Survey of COVID-19 Mortality Dynamics Using Machine Learning Techniques

The pandemic COVID-19 (SARS-CoV-2) known as the coronavirus is spreading at a very alarming rate in the world which is quite a threatening situation. In terms of disease presentation, the global epidemic COVID-19 can be traced to the ancestral influenza viruses. COVID-19 outbreak is a very perturbing situation for the high rate of mortality as reported in recent studies. The mortality and morbidity rates have always been a concerning issue in the determination of the scale of epidemics. These mortality rate predictions subsume an interesting factor within them i.e. variance of mortality rates across regions. The advancement in machine learning-based tools is a vital component corresponding to addressing the 'heterogeneity in fatality rates'. This review envelopes the recent developments in mortality rate predictions for COVID-19. The review helps in gaining an insight into Spatio-temporal mortality dynamics which will be fruitful with reference to future control-strategy implementation. By observing the Spatio-temporal pattern of mortality dynamics, the inter-dependence of various factors can be explored, and hence, mortality rates can be reduced in the upcoming time. © 2021 IEEE.