Universiti Teknologi Malaysia Institutional Repository

Covid-19 severity classification using supervised learning approach

Mohamand Noor, Nurul Fathia and Sipail, Herold Sylvestro and Ahmad, Norulhusna and Mohd. Noor, Norliza (2021) Covid-19 severity classification using supervised learning approach. In: 1st National Biomedical Engineering Conference, NBEC 2021, 9 - 10 November 2021, Virtual, Online.

Full text not available from this repository.

Official URL: http://dx.doi.org/10.1109/NBEC53282.2021.9618747


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.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Covid-19 Severity Classification, Supervised Machine Learning Technique
Subjects:T Technology > T Technology (General)
Divisions:Razak School of Engineering and Advanced Technology
ID Code:96615
Deposited By: Widya Wahid
Deposited On:15 Aug 2022 11:09
Last Modified:15 Aug 2022 11:09

Repository Staff Only: item control page