  For severely affected COVID-19 patients, it is crucial to identify high-risk
patients and predict survival and need for intensive care (ICU). Most of the
proposed models are not well reported making them less reproducible and prone
to high risk of bias particularly in presence of imbalance data/class. In this
study, the performances of nine machine and deep learning algorithms in
combination with two widely used feature selection methods were investigated to
predict last status representing mortality, ICU requirement, and ventilation
days. Fivefold cross-validation was used for training and validation purposes.
To minimize bias, the training and testing sets were split maintaining similar
distributions. Only 10 out of 122 features were found to be useful in
prediction modelling with Acute kidney injury during hospitalization feature
being the most important one. The algorithms performances depend on feature
numbers and data pre-processing techniques. LSTM performs the best in
predicting last status and ICU requirement with 90%, 92%, 86% and 95% accuracy,
sensitivity, specificity, and AUC respectively. DNN performs the best in
predicting Ventilation days with 88% accuracy. Considering all the factors and
limitations including absence of exact time point of clinical onset, LSTM with
carefully selected features can accurately predict last status and ICU
requirement. DNN performs the best in predicting Ventilation days. Appropriate
machine learning algorithm with carefully selected features and balance data
can accurately predict mortality, ICU requirement and ventilation support. Such
model can be very useful in emergency and pandemic where prompt and precise
