Covid Symptom Severity Using Decision Tree

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Naim Rochmawati, Hanik Badriyah Hidayati, Yuni Yamasari, Wiyli Yustanti, Lusia Rakhmawati, Hapsari P. A. Tjahyaningtijas, Yeni Anistyasari

2020 Proceeding - 2020 3rd International Conference on Vocational Education and Electrical Engineering: Strengthening the framework of Society 5.0 through Innovations in Education, Electrical, Engineering and Informatics Engineering, ICVEE 2020 Conference paper Cited by 43 Quartile

Abstract

Corona is a very contagious virus. In a pandemic like this, people often worry whether they are infected or not. When they cough, they often worry whether it is a sign of covid-19 or an ordinary cough. From the clinical symptoms can actually be known whether someone has Covid or not. In thisstudy, a clinical symptom dataset will be used to classify the symptoms using a Decision Tree algorithm. The decision trees used in this research are J48 and Hoeffding Tree. Decision Tree is one of the most popular classification methods because it is easy to interpret by Humans. the prediction model uses a hierarchical structure. The concept is to convert data into decision trees or decision rules. the result of J48 were slightly better than the Hoeffding tree in terms of accuracy, precision, and recall. Meanwhile, from the tree view results, the Hoeffding Tree is simpler and the numberof nodes is less than J48. © 2020 IEEE.

Affiliations

Universitas Negeri Surabaya, Department of Informatics, Universitas Airlangga, Department of Neurology, Surabaya, Indonesia