Predicting Severity Levels in COVID-19 using Deep Convolutional Neural Network classifiers

  • Bandaru Sandeep, Buggaveeti Mitra, Buggaveeti Teja


The rapid spread of COVID-19 has brought together doctors, researchers, and data scientists to find a solution. Scientists are using sophisticated technologies, such as big data analytics, machine learning, and natural language processing for tracking the virus and learning more about it.  On the one hand, the excess of stored data has considerably increased the opportunities to interrelate and analyze them. This paper proposed to predict the severity level and early prediction of COVID-19 using DCNNCC (Deep Convolutional Neural Network Cross Classifier). This paper is composed of '3' steps, namely, disease prediction, severity level analysis, and early prediction. In the first phase, initially, the dataset is preprocessed, then the important features are extracted from the dataset, and finally, the disease is clustered into positive and predictive using TNKM. In phase 2, the proposed system utilizes DCNNCC for severity analysis, which classifies a high, low, and moderate level.  In phase 3, the non-coronavirus data undergoes preprocessing, and then important features are extracted from the dataset. Finally, the potential level of the patient against the coronavirus is predicted by the BDR method.