Machine Learning-based Early Detection and Prognosis of the Covid-19Pandemic

Machine Learning-based Early Detection and Prognosis of the Covid-19Pandemic

Covid-19;data preprocessing;healthcare;machine learning;predictiveanalysis;web tool

The outbreak of Covid-19has caused a global health crisis, presenting numerous challenges to the healthcare system with its severe respiratory symptoms and variable presentation. Early and accurate diagnosis of the virus is critical in controlling its spread and reducing the burden on healthcare facilities. To address this issue and relieve the strain on the healthcare system, this paperproposes a machine learning-based approach for Covid-19diagnosis. Four algorithms were used for analyzing early Covid-19 detection,i.e., logistic regression, random forest, decision tree, and naive Bayes,using a data setof basic symptoms such as fever, shortness of breath, etc. for predicting positive and negative Covid-19cases. Furthermore, development of a web portal that provides information on global vaccine distribution and the most widely used vaccinesby country along with Covid-19predictions. Our evaluation results demonstrate that the decision tree modeloutperformedthe other models, achieving an accuracy of 97.69%. This study providesa practical solution to the ongoing Covid-19crisis through an improved diagnosis method and access to vaccination information

Ajitha Santhakumari*, R. Shilpa& Hudhaifa Mohammed Abdulwahab

https://journals.itb.ac.id/index.php/jictra/article/view/17709/6310

Department of Computer Application, Ramaiah Institute of Technology,(Affiliated to VTU), Bangalore 560054, Karnataka, India

2023

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