An explainable multi-class decision support framework to predict COVID-19 prognosis utilizing biomarkers

Abstract

Millions of lives have been impacted by COVID-19, which has spread rapidly. Several vaccines have been developed to curb the severe prognosis induced by the virus. However, a part of the population (elderly and patients with coexisting conditions) is still at risk. It is crucial to identify these patients early since appropriate treatments can be provided to them to prevent the onset of severe symptoms such as breathlessness and hypoxia. Hence, this study utilizes machine learning and explainable artificial intelligence (XAI) to predict COVID-19 severity using biochemical, haematological and inflammatory markers. The patients are grouped into three classes: mild, moderate and severe. Four nature-inspired techniques have been utilized to select the best markers. The final stacked model obtained a maximum accuracy of 84. Demystifying the models has been done using four XAI techniques, including Shapley additive values (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME). Lactate dehydrogenase (LDH), albumin, D-Dimer, c-reactive protein (CRP) and lymphocytes were considered important, according to them. The classifiers can be utilized as a prognostic decision support framework to aid the medical personnel in classifying COVID-19 patients.