A Machine Learning Approach for Diagnosing and Identifying Symptoms of COVID-19
2022 International Conference on Emerging Trends in Computing and Engineering Applications, ETCEA 2022
; 2022.
Article
in English
| Scopus | ID: covidwho-2227912
ABSTRACT
Coronavirus 2019 (COVID-19) is a pandemic that hit the world and was responsible for the death of millions and the life disruption of billions of people. One of the most critical challenges faced during the earlier breakthrough of the diseases was identifying symptoms confused with colds, flu, and other common infections. Nevertheless, despite all the effort and research conducted for this purpose, this challenge continues as more strains, variants, and mutations appear. This work presents a solution for this problem based on machine learning classification and variable importance algorithms. A public dataset of 274,957 cases has been classified into typical and COVID-19 cases based on the reported symptoms and other variables. The dataset was used for classifying the reported cases using K-nearest neighbor (KNN), Naïve Bayes, and Decision Trees (DT) algorithms and identifying the significant symptoms that were decisive in classifying the patients using Gini, Information Gain, and Information Gain Ratio algorithms. Naïve Bayes and Decision Trees performed best with a Classification Accuracy (CA) score of 95.2% and 96.3%, respectively. The Naïve Bayes classifier scored an Area Under the Curve (AUC) of 88.75%. In addition, the applied variable importance algorithms identified headache, fever, and sore throat as the most important symptoms. © 2022 IEEE.
COVID-19 / SARS-Cov-2; Data Mining; Health Informatics; Machine Learning; Medical Diagnosis; Classification (of information); Decision trees; Diagnosis; Medical informatics; Nearest neighbor search; Coronaviruses; Critical challenges; Machine learning approaches; Machine-learning; Naive bayes; On-machines; Problem-based; Variable importances; COVID-19
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Topics:
Variants
Language:
English
Journal:
2022 International Conference on Emerging Trends in Computing and Engineering Applications, ETCEA 2022
Year:
2022
Document Type:
Article
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