Download PDFOpen PDF in browser

Early Prediction of Covid-19 Disease with Machine and Deep Learning Approaches

EasyChair Preprint no. 4867, version 2

Versions: 12history
16 pagesDate: January 6, 2021


The coronavirus which causes COVID-19 disease has become a pandemic and has exposed all over the world and the cases are increasing daily .So that by using predictive algorithms we can predicate the diseases easily.  Here, we perform clinical predictive models that estimate, using deep learning and laboratory data, which patients are likely to receive a COVID-19 diseases. Some patients with coronavirus disease 2019 (COVID-19) show abnormal changes in laboratory myocardial injury markers, suggesting that patients with myocardial injury have a higher mortality rate than those without myocardial injury. This reviews possible mechanism of myocardial injury in patients with COVID-19. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) affects the patients with COVID-19 in aspects of direct infection of myocardial injury, specific binding to functional receptors on cardiomyocytes, and immune-mediated myocardial injury. During hospitalization, the monitoring of laboratory myocardial injury markers in patients of COVID-19 should be strengthened. So it takes time to interpret the laboratory findings thus the limitations in terms of both treatment and findings are emerged. Due to such limitations, the need fir clinical decision making system with predictive algorithms has arisen.

Keyphrases: ANN, CNN, CNNLSTM, CNNRNN, COVID-19, Covid-19 Dataset, LSTM, machine learning, RNN

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Harshvardhan Tiwari and K R Sinchana and Preeti V Patil and Shiji K Shridhar and G Aishwarya},
  title = {Early Prediction of Covid-19 Disease with Machine and Deep Learning Approaches},
  howpublished = {EasyChair Preprint no. 4867},

  year = {EasyChair, 2021}}
Download PDFOpen PDF in browser