EXPRESS: Clinical Predictions of COVID-19 Patients Using Deep Stacking Neural Network.

Abstract

The COVID-19 pandemic, which emerged in late 2019, has caused millions of infections and fatalities globally, disrupting various aspects of human society, including socio-economic, political and educational systems. One of the key challenges during the COVID-19 pandemic is accurately predicting the clinical development and outcome of the infected patients. In response, scientists and medical professionals globally have mobilized to develop prognostic strategies such as risk scores, biomarkers, and machine learning models to predict the clinical course and outcome of COVID-19 patients. In this contribution, we deployed a mathematical approach called matrix factorization feature selection (MFFS) to select the most relevant features from the anonymized laboratory biomarkers and demographic data of COVID-19 patients. Based on these features, we developed a model that can assist clinical care by using the deep stacking neural network (DSNN) to predict patients mortality risk. To gauge the performance of our suggested model, we performed a comparative analysis with principal component analysis plus support vector machine, deep learning, and random forest, achieving outstanding performances. The DSNN model outperformed all the other models in terms of area under the curve (96.0%), 1-score (98.1%), recall (98.5%), accuracy (99.0%), precision (97.7%), specificity (97.0%), and maximum probability of correction decision (93.4%). Our model outperforms the clinical predictive models regarding patient mortality risk and classification in the literature. Therefore, we conclude that our robust model can help healthcare professionals to manage COVID-19 patients more effectively. We expect that early prediction of COVID-19 patients and preventive interventions can reduce the mortality risk of patients.