Covid-19 Severity Classification Using Supervised Learning Approach
1st National Biomedical Engineering Conference, NBEC 2021
; : 151-156, 2021.
Article
in English
| Scopus | ID: covidwho-1672837
ABSTRACT
This paper presented work on supervised machine learning techniques using K-NN, Linear SVM, Naïve Bayes, Decision Tree (J48), Ada Boost, Bagging and Stacking for the purpose to classify the severity group of covid-19 symptoms. The data was obtained from Kaggle dataset, which was obtained through a survey collected from the participant with varying gender and age that had visited 10 or more countries including China, France, Germany Iran, Italy, Republic of Korean, Spain, UAE, other European Countries (Other-EUR) and Others. The survey is Covid-19 symptom based on guidelines given by the World Health Organization (WHO) and the Ministry of Health and Family Welfare, India which then classified into 4 different levels of severity, Mild, Moderate, Severe, and None. The results from the seven classifiers used in this study showed very low classification results. © 2021 IEEE.
Covid-19 Severity Classification; Data Mining; Supervised Machine Learning Technique; Weka; Classification (of information); Decision trees; Learning algorithms; Nearest neighbor search; Support vector machines; Surveys; Bayes decision; Linear SVM; Machine learning techniques; Naive bayes; Stackings; Supervised learning approaches; Supervised machine learning
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
1st National Biomedical Engineering Conference, NBEC 2021
Year:
2021
Document Type:
Article
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