Cargando…
Realizing an Effective COVID-19 Diagnosis System Based on Machine Learning and IoT in Smart Hospital Environment
The aim of this study is to propose a model based on machine learning (ML) and Internet of Things (IoT) to diagnose patients with COVID-19 in smart hospitals. In this sense, it was emphasized that by the representation for the role of ML models and IoT relevant technologies in smart hospital environ...
Formato: | Online Artículo Texto |
---|---|
Lenguaje: | English |
Publicado: |
IEEE
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8769008/ https://www.ncbi.nlm.nih.gov/pubmed/35782183 http://dx.doi.org/10.1109/JIOT.2021.3050775 |
_version_ | 1784635035861647360 |
---|---|
collection | PubMed |
description | The aim of this study is to propose a model based on machine learning (ML) and Internet of Things (IoT) to diagnose patients with COVID-19 in smart hospitals. In this sense, it was emphasized that by the representation for the role of ML models and IoT relevant technologies in smart hospital environment. The accuracy rate of diagnosis (classification) based on laboratory findings can be improved via light ML models. Three ML models, namely, naive Bayes (NB), Random Forest (RF), and support vector machine (SVM), were trained and tested on the basis of laboratory datasets. Three main methodological scenarios of COVID-19 diagnoses, such as diagnoses based on original and normalized datasets and those based on feature selection, were presented. Compared with benchmark studies, our proposed SVM model obtained the most substantial diagnosis performance (up to 95%). The proposed model based on ML and IoT can be served as a clinical decision support system. Furthermore, the outcomes could reduce the workload for doctors, tackle the issue of patient overcrowding, and reduce mortality rate during the COVID-19 pandemic. |
format | Online Article Text |
id | pubmed-8769008 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | IEEE |
record_format | MEDLINE/PubMed |
spelling | pubmed-87690082022-06-29 Realizing an Effective COVID-19 Diagnosis System Based on Machine Learning and IoT in Smart Hospital Environment IEEE Internet Things J Article The aim of this study is to propose a model based on machine learning (ML) and Internet of Things (IoT) to diagnose patients with COVID-19 in smart hospitals. In this sense, it was emphasized that by the representation for the role of ML models and IoT relevant technologies in smart hospital environment. The accuracy rate of diagnosis (classification) based on laboratory findings can be improved via light ML models. Three ML models, namely, naive Bayes (NB), Random Forest (RF), and support vector machine (SVM), were trained and tested on the basis of laboratory datasets. Three main methodological scenarios of COVID-19 diagnoses, such as diagnoses based on original and normalized datasets and those based on feature selection, were presented. Compared with benchmark studies, our proposed SVM model obtained the most substantial diagnosis performance (up to 95%). The proposed model based on ML and IoT can be served as a clinical decision support system. Furthermore, the outcomes could reduce the workload for doctors, tackle the issue of patient overcrowding, and reduce mortality rate during the COVID-19 pandemic. IEEE 2021-01-11 /pmc/articles/PMC8769008/ /pubmed/35782183 http://dx.doi.org/10.1109/JIOT.2021.3050775 Text en This article is free to access and download, along with rights for full text and data mining, re-use and analysis. |
spellingShingle | Article Realizing an Effective COVID-19 Diagnosis System Based on Machine Learning and IoT in Smart Hospital Environment |
title | Realizing an Effective COVID-19 Diagnosis System Based on Machine Learning and IoT in Smart Hospital Environment |
title_full | Realizing an Effective COVID-19 Diagnosis System Based on Machine Learning and IoT in Smart Hospital Environment |
title_fullStr | Realizing an Effective COVID-19 Diagnosis System Based on Machine Learning and IoT in Smart Hospital Environment |
title_full_unstemmed | Realizing an Effective COVID-19 Diagnosis System Based on Machine Learning and IoT in Smart Hospital Environment |
title_short | Realizing an Effective COVID-19 Diagnosis System Based on Machine Learning and IoT in Smart Hospital Environment |
title_sort | realizing an effective covid-19 diagnosis system based on machine learning and iot in smart hospital environment |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8769008/ https://www.ncbi.nlm.nih.gov/pubmed/35782183 http://dx.doi.org/10.1109/JIOT.2021.3050775 |
work_keys_str_mv | AT realizinganeffectivecovid19diagnosissystembasedonmachinelearningandiotinsmarthospitalenvironment AT realizinganeffectivecovid19diagnosissystembasedonmachinelearningandiotinsmarthospitalenvironment AT realizinganeffectivecovid19diagnosissystembasedonmachinelearningandiotinsmarthospitalenvironment AT realizinganeffectivecovid19diagnosissystembasedonmachinelearningandiotinsmarthospitalenvironment AT realizinganeffectivecovid19diagnosissystembasedonmachinelearningandiotinsmarthospitalenvironment AT realizinganeffectivecovid19diagnosissystembasedonmachinelearningandiotinsmarthospitalenvironment AT realizinganeffectivecovid19diagnosissystembasedonmachinelearningandiotinsmarthospitalenvironment |