Exploring Biomarker Identification and Mortality Prediction of COVID-19 Patients Using ML Algorithms

ABSTRACT

The coronavirus pandemic led to the collapse of the healthcare systems of several countries worldwide, including the highly developed ones. The sudden rise in hospitalization requirements for the patients suffering from the disease, caused a tremendous pressure not only on the healthcare system but also on the frontline workers. So, for early diagnosis and prognosis of the patients, identification of the biomarkers pertaining to the coronavirus disease became an essential requirement. Thus, a machine learning (ML) based mortality prediction model was developed that was able to predict the mortality of the patients using a combination of only six features. The six selected features included, four identified biomarkers, namely, lactate dehydrogenase (LDH), neutrophils percentage (NP), fibrin degradation products (FDP), and erythrocyte sedimentation rate (ESR);and, other two features as age and the coronavirus detection test. The developed model with a novel semiautomated method of medical data handling technique, achieved an accuracy of over 98%, and was able to predict the final outcome of the patients on an average of 8 days in advance. The corresponding work was carried out with the intent to ease the burden on the healthcare system, by providing a faster and accurate clinical assessment of the patients suffering from the coronavirus disease. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.