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Covid19 Prediction Using Machine Learning Algorithms: a Comparative Study

EasyChair Preprint no. 10213

9 pagesDate: May 18, 2023


Artificial intelligence has been used in many ways to combat the COVID-19 pandemic caused by the SARSCoV-2 virus. One such approach is to use machine learning algorithms to predict different virus variants. By analyzing large volumes of genomic data, machine learning algorithms can identify patterns and make predictions about the behavior and characteristics of different viral strains.


This article presents a comparative study aimed at identifying the most effective machine learning algorithm for developing an approach based on artificial intelligence and machine learning methods to combat the virus.


The study evaluated a total of forty algorithms, including Decision Tree, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest, and Artificial Neural Network (ANN), among others. we analyzed each algorithm for its performance using criteria such as RMSE, R-Squared and time taken, using data from the Chembl database.


To do this, we using the latest information from biological publications and medical reports in order to carefully select inputs and targets.

Keyphrases: Artificial Intelligence, COVID-19, genome, machine learning, Pandemic

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Imane Aitouhanni},
  title = {Covid19 Prediction Using Machine Learning Algorithms: a Comparative Study},
  howpublished = {EasyChair Preprint no. 10213},

  year = {EasyChair, 2023}}
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