A Meta-analysis of Machine Learning for the Diagnosis of Covid-19 Disease
2022 IEEE International Conference on Data Science and Information System, ICDSIS 2022
; 2022.
Article
in English
| Scopus | ID: covidwho-2136227
ABSTRACT
Coronavirus 2019 has wreaked havoc on people's lives all across the globe. The number of positive cases is increasing, and the Asian country is now one of the most severely impacted. This article examines machine learning models that are more accurate at predicting covid. Based on the data from China, regression-based, decision tree-based, naive Bayes, and random forest-based models were developed and verified on a sample from India. A data-driven strategy with better precision, such as the one used here, is beneficial for the government and public to respond in a proactive manner. This study reveals that the suggested framework has superior capabilities in detecting COVID-19. © 2022 IEEE.
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Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Reviews
Language:
English
Journal:
2022 IEEE International Conference on Data Science and Information System, ICDSIS 2022
Year:
2022
Document Type:
Article
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