Leveraging LSTM-based Auto-Encoders for Covid-19 Severity Prediction and Classification

The primary purpose of this study activity is to give its share to the battle against the growing threats posed by COVID-19 all over the world, which has over one million cases. According to the World Health Organisation (WHO), more than seven million individuals have been reported to have passed away as a result of; the work is focused on building an optimization-based deep learning model that will assist in identifying COVID-19 patients based on early clinical information. The suggested model is pretty accurate for the prediction of COVID-19 and improves the strictness of the forecasts of the ailment. This is accomplished by combining the medical and personal data from a variety of populations. In the preliminary stage, the only things that can be determined are the pre-processing phase and the characteristics that are pertinent for classification using ESDO. The efficacy is characterised by the use of a classifier that combines LSTM and AE. This allows for the determination of the degree of severity, which may range from mild to moderate to severe, with a precision of $\mathbf{9 9 \%} \mathbf{. 0. 0 2}$, and a level of accuracy of $\mathbf{9 8}$. Therefore, future study will concentrate on applying the offered approach for the affected regions in India exclusively, and efforts will also be made to compare the suggested methodology to larger databases, if any such databases exist.