Symptom Analysis Using Fuzzy Logic for Detection and Monitoring of COVID-19 Patients

Recent developments regarding the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) opened new horizons of healthcare opportunities. Moreover, these technological advancements give strength to face upcoming healthcare challenges. One of such challenges is the advent o...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Tayyaba Ilyas, Danish Mahmood, Ghufran Ahmed, Adnan Akhunzada
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Artificial Intelligence COVID-19 detection E-Health fusion algorithm fuzzy logic Technology T
Acceso en línea:	https://doaj.org/article/5db06c1eba984e3ea36fe00df425976c
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:5db06c1eba984e3ea36fe00df425976c
record_format	dspace
spelling	oai:doaj.org-article:5db06c1eba984e3ea36fe00df425976c2021-11-11T15:50:30ZSymptom Analysis Using Fuzzy Logic for Detection and Monitoring of COVID-19 Patients10.3390/en142170231996-1073https://doaj.org/article/5db06c1eba984e3ea36fe00df425976c2021-10-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/21/7023https://doaj.org/toc/1996-1073Recent developments regarding the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) opened new horizons of healthcare opportunities. Moreover, these technological advancements give strength to face upcoming healthcare challenges. One of such challenges is the advent of COVID-19, which has adverse effects beyond comprehension. Therefore, utilizing the basic functionalities of IoT, this work presents a real-time rule-based Fuzzy Logic classifier for COVID-19 Detection (FLCD). The proposed model deploys the IoT framework to collect real-time symptoms data from users to detect symptomatic and asymptomatic Covid-19 patients. Moreover, the proposed framework is also capable of monitoring the treatment response of infected people. FLCD constitutes three components: symptom data collection using wearable sensors, data fusion through Rule-Based Fuzzy Logic classifier, and cloud infrastructure to store data with a possible verdict (normal, mild, serious, or critical). After extracting the relevant features, experiments with a synthetic COVID-19 symptom dataset are conducted to ensure effective and accurate detection of COVID-19 cases. As a result, FLCD successfully acquired 95% accuracy, 94.73% precision, 93.35% recall, and showed a minimum error rate of 2.52%.Tayyaba IlyasDanish MahmoodGhufran AhmedAdnan AkhunzadaMDPI AGarticleArtificial IntelligenceCOVID-19detectionE-Healthfusion algorithmfuzzy logicTechnologyTENEnergies, Vol 14, Iss 7023, p 7023 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Artificial Intelligence COVID-19 detection E-Health fusion algorithm fuzzy logic Technology T
spellingShingle	Artificial Intelligence COVID-19 detection E-Health fusion algorithm fuzzy logic Technology T Tayyaba Ilyas Danish Mahmood Ghufran Ahmed Adnan Akhunzada Symptom Analysis Using Fuzzy Logic for Detection and Monitoring of COVID-19 Patients
description	Recent developments regarding the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) opened new horizons of healthcare opportunities. Moreover, these technological advancements give strength to face upcoming healthcare challenges. One of such challenges is the advent of COVID-19, which has adverse effects beyond comprehension. Therefore, utilizing the basic functionalities of IoT, this work presents a real-time rule-based Fuzzy Logic classifier for COVID-19 Detection (FLCD). The proposed model deploys the IoT framework to collect real-time symptoms data from users to detect symptomatic and asymptomatic Covid-19 patients. Moreover, the proposed framework is also capable of monitoring the treatment response of infected people. FLCD constitutes three components: symptom data collection using wearable sensors, data fusion through Rule-Based Fuzzy Logic classifier, and cloud infrastructure to store data with a possible verdict (normal, mild, serious, or critical). After extracting the relevant features, experiments with a synthetic COVID-19 symptom dataset are conducted to ensure effective and accurate detection of COVID-19 cases. As a result, FLCD successfully acquired 95% accuracy, 94.73% precision, 93.35% recall, and showed a minimum error rate of 2.52%.
format	article
author	Tayyaba Ilyas Danish Mahmood Ghufran Ahmed Adnan Akhunzada
author_facet	Tayyaba Ilyas Danish Mahmood Ghufran Ahmed Adnan Akhunzada
author_sort	Tayyaba Ilyas
title	Symptom Analysis Using Fuzzy Logic for Detection and Monitoring of COVID-19 Patients
title_short	Symptom Analysis Using Fuzzy Logic for Detection and Monitoring of COVID-19 Patients
title_full	Symptom Analysis Using Fuzzy Logic for Detection and Monitoring of COVID-19 Patients
title_fullStr	Symptom Analysis Using Fuzzy Logic for Detection and Monitoring of COVID-19 Patients
title_full_unstemmed	Symptom Analysis Using Fuzzy Logic for Detection and Monitoring of COVID-19 Patients
title_sort	symptom analysis using fuzzy logic for detection and monitoring of covid-19 patients
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/5db06c1eba984e3ea36fe00df425976c
work_keys_str_mv	AT tayyabailyas symptomanalysisusingfuzzylogicfordetectionandmonitoringofcovid19patients AT danishmahmood symptomanalysisusingfuzzylogicfordetectionandmonitoringofcovid19patients AT ghufranahmed symptomanalysisusingfuzzylogicfordetectionandmonitoringofcovid19patients AT adnanakhunzada symptomanalysisusingfuzzylogicfordetectionandmonitoringofcovid19patients
_version_	1718433573843763200

Symptom Analysis Using Fuzzy Logic for Detection and Monitoring of COVID-19 Patients

Ejemplares similares