A Case-Based Reasoning for Detection Coronavirus (Covid-19) Using Cosine Similarity

DOI

10.2991/aer.k.220131.030How to use a DOI?

Keywords

Coronavirus Detection; Artificial Intelligence; Early Detection; Cosine Similarity Method

Abstract

Case-based reasoning is a new approach that can be used to diagnose disease in addition to using expert systems or other approaches, which are part of artificial intelligence. Case-based reasoning can diagnose diseases based on visible or perceived clinical symptoms. This study tries to build case-based reasoning for early detection of COVID-19 by looking at the characteristics of clinical symptoms seen in a person using the Cosine Similarity method. Cosine similarity is a method to find level of similarity between two cases. The detection process is carried out by entering a new case containing symptoms into the system, then system will perform a similarity calculation process between the old case and the new case. The results show case-based reasoning for early detection of COVID-19 using the Cosine Similarity method can detect a similarity level of 80%.

Copyright

© 2022 The Authors. Published by Atlantis Press International B.V.

Open Access

This is an open access article under the CC BY-NC license.

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TY  - CONF
AU  - Murien Nugraheni
AU  - Widodo
AU  - Irma Permata Sari
PY  - 2022
DA  - 2022/02/01
TI  - A Case-Based Reasoning for Detection Coronavirus (Covid-19) Using Cosine Similarity
BT  - Proceedings of the Conference on Broad Exposure to Science and Technology 2021 (BEST 2021)
PB  - Atlantis Press
SP  - 178
EP  - 183
SN  - 2352-5401
UR  - https://doi.org/10.2991/aer.k.220131.030
DO  - 10.2991/aer.k.220131.030
ID  - Nugraheni2022
ER  -