Covid-19 Progression Forecasting and Mortality Rate Analysis for Genome Clinical Characteristics and Chest CT Scan
Keywords:
CLAHE, 3D U-Net Segmentation, Chi-Square Test, ResNet50 and DenseNet121Abstract
The COVID-19 sickness has spread over the world as a result of SARS-CoV2 turning into a pandemic. To reduce the strain on healthcare systems, the decision-making model with prediction methodology for clinical data is required. The foremost objective of the proposed work is towards the analysis of COVID-19 disease mortality rate for patients with the help of genomic clinical data and predicting the progression levels of disease spread. Both the datasets are analyzed individually for the COVID and NON COVID patients. In Genomic clinical features, the significant mortality of disease is identified with the help of Chi-square test based on the P-value. The Chest CT scan utilizes 3D U-Net segmentation with a hybrid transfer learning algorithm such as ResNet50 and DenseNet121 along with metaheuristic optimization that reduces time complexity and classifies the disease progression levels of COVID-19. Jupyter Notebook simulations are used to implement the suggested technique. In addition, it is assessed using Accuracy, Sensitivity, Specificity, and Precision. Experimental results make it obvious that the purpose of a proposed prediction model has a 99.07% accuracy rate, a 97.16% Precision rate, a 97.66% Recall rate, and a 97.16% f1 – Score in identifying Progression of COVID-19 affected patients. The accuracy rate depends on the progression stages for No progression, Mild, and Highly Risk are 97.22%, 100%, and 100%. These results imply that medical specialists can benefit from the proposed methodology by using it to forecast COVID-19 disease for experimental prophecy investigation.
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