COVID-19 Severity Prediction Using Combined Machine Learning and Transfer Learning Approaches

Abstract

Abstract The global spread of Coronavirus Disease 2019 (COVID-19) has resulted in an extensive pandemic, with the virus rapidly transmitting through interactions among infected individuals, presenting a substantial threat to healthcare professionals. In response, computer scientists have employed artificial intelligence methodologies to identify and address COVID-19. This study utilizes machine learning and transfer learning techniques to forecast the severity of the coronavirus, aiding healthcare providers in determining the progression of the illness in patients. Prediction of disease severity occurs in two stages. Initially, blood parameter values are utilized for preliminary screening of coronavirus infection through machine learning methods. The first stage employs the proposed Probabilistic Stacked Ensemble Classifier, employing optimal features selected using the proposed Modified Mutual Information feature selection algorithm, to detect the presence or absence of the virus. Following that, the subsequent phase employs proposed mResNet-50, a transfer learning approach, which utilizes Computed Tomography (CT)-scan images to predict the stage of infection in affected individuals. Experimental results indicate that the model achieves a 97.79% accuracy rate in forecasting infection stages and demonstrates the generalizability of the proposed model across benchmark datasets.

Keywords

• Machine Learning
• Transfer Learning
• COVID-19
• clinical data
• CT-Scan