Clusterization of Clinical Symptoms of Disease COVID-19 Using K: Means Algorithm Based on Arduino Uno and Sensor Max 30100

Maria Ulfah, Andi Irtawaty

2022

Abstract

Symptoms of Covid-19 other than fever, which must be watched out by the public at this time is the condition of shortness of breath due to a decrease in oxygen levels in the blood. This condition can make a person experience respiratory problems such as shortness of breath or dyspnea which can be fatal, especially for people who can interfere with vital organs, namely the heart and oxygen levels in the body to cause death. This study designed a tool that can detect clinical symptoms of COVID-19 disease, namely shortness of breath through measuring oxygen saturation levels and heart rate for shortness of breath symptoms using the MAX30100 sensor and Arduino Uno as a controller. Tests for oxygen saturation levels and heart rate were carried out by measuring the thumb and followed by testing the accuracy of the output using the clustering technique using the K-Means Algorithm using the Rapid Miner Application. Comparison of readings of clinical symptoms of shortness of breath through oxygen saturation levels. heart rate from the output of the device compared with the use of the K-Means clustering technique showed the same results, which was 100%. Tests 1-10,19,20 showed no symptoms of covid-19 while testing 11-18 showed symptoms of covid-19.

Paper Citation

in Harvard Style

Ulfah M. and Irtawaty A. (2022). Clusterization of Clinical Symptoms of Disease COVID-19 Using K: Means Algorithm Based on Arduino Uno and Sensor Max 30100. In Proceedings of the 5th International Conference on Applied Science and Technology on Engineering Science - Volume 1: iCAST-ES; ISBN 978-989-758-619-4, SciTePress, pages 597-600. DOI: 10.5220/0011843900003575

in Bibtex Style

@conference{icast-es22,
author={Maria Ulfah and Andi Irtawaty},
title={Clusterization of Clinical Symptoms of Disease COVID-19 Using K: Means Algorithm Based on Arduino Uno and Sensor Max 30100},
booktitle={Proceedings of the 5th International Conference on Applied Science and Technology on Engineering Science - Volume 1: iCAST-ES},
year={2022},
pages={597-600},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011843900003575},
isbn={978-989-758-619-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 5th International Conference on Applied Science and Technology on Engineering Science - Volume 1: iCAST-ES
TI - Clusterization of Clinical Symptoms of Disease COVID-19 Using K: Means Algorithm Based on Arduino Uno and Sensor Max 30100
SN - 978-989-758-619-4
AU - Ulfah M.
AU - Irtawaty A.
PY - 2022
SP - 597
EP - 600
DO - 10.5220/0011843900003575
PB - SciTePress