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Framework for Analysis and Prediction of Risk Factors of Covid-19
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 184 - Number 36 |
| Year of Publication: 2022 |
| Authors: Devanshu Shende, Supreeth Shetty, Akram Shaikh, Rugved Sakure, Ganesh Deshmukh |
10.5120/ijca2022922452
|
Devanshu Shende, Supreeth Shetty, Akram Shaikh, Rugved Sakure, Ganesh Deshmukh . Framework for Analysis and Prediction of Risk Factors of Covid-19. International Journal of Computer Applications. 184, 36 ( Nov 2022), 5-11. DOI=10.5120/ijca2022922452
Abstract
In March 2020 Covid-19 was declared a global pandemic. There was a shortage of hospital beds all across the country. Because of this, an efficient resource allocation system is essential. This can be achieved through severity prediction of patients on hospital admission and allotting proper resources to the most severe cases first. In this study, a severity prediction model has been described which is made using Deep Neural Networks. It predicts how severe a patient will possibly get, using 26 clinical factors like Hemoglobin, HCT, RBC, etc. The model showed an accuracy of around 85%. RT-PCR is currently used as the primary test to detect Covid-19, but studies have shown it is not very reliable as it can give false negatives during the early stages of the disease. To tackle this problem, a CNN model has been applied to a dataset of chest x-ray images of patients who were tested for Covid-19. The model showed 98.33% accuracy for Covid-19 detection. Statistical analysis and data visualization modules for Covid-19 are included in the framework for researchers, the general public, and the healthcare system to keep track of the global pandemic.
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