Prognosis Patients with COVID-19 using Deep Learning

Abstract

Background: Prognostics study the prediction of an event before it happens, to enable critical decision making to be more efficient. The prognostics are very useful for front line physicians to predict how a disease may affect a patient and react accordingly to save the patients’ lives. The coronavirus (COVID-19) is novel and not enough knowledge about the virus’ behaviour and Key performance indicators (KPIs) to assess the mortality risk prediction. However, using a lot of complex and expensive medical biomarkers could be impossible for many low-budget hospitals. This motivates the development of a prediction model that not only maximizes performance but does so using the least number of biomarkers possible. Methods: For the mortality risk prediction, this research work proposes aCOVID-19 mortality risk calculator based on a Deep Learning (DL) model, and based on a data set provided by the HM Hospitals from Madrid, Spain. A pre-processing strategy for unbalanced classes and feature selection is proposed. Results: The DL model is tested, and the following results are achieved include area under the curve (AUC) 0.93, F2 score 0.93, recall 1.00, accuracy, 0.95, precision 0.91, specificity 0.9279 and maximum probability of correct decision(MPCD) 0.93. Conclusion: The MPCD score shows that the proposed DL outperforms on the everyday set when evaluating even with an over-sampling technique. The benefits of imputating unavailable biomarker data are also evaluated. The results are compared against a random forest (RF) algorithm and the newly proposed methods. The results show that the proposed method is significantly best for the risk prediction of the patients with COVID-19.