Symptom-Based COVID-19 Prognosis through AI-Based IoT: A Bioinformatics Approach.
Biomed Res Int
; 2022: 3113119, 2022.
Article
in English
| MEDLINE | ID: covidwho-1973955
ABSTRACT
Objective:
Internet of Things (IoT) integrates several technologies where devices learn from the experience of each other thereby reducing human-intervened likely errors. Modern technologies like IoT and machine learning enable the conventional to patient-specific approach transition in healthcare. In conventional approach, the biggest challenge faced by healthcare professionals is to predict a disease by observing the symptoms, monitoring the remote area patient, and also attending to the patient all the time after being hospitalised. IoT provides real-time data, makes decision-making smarter, and provides far superior analytics, and all these to help improve the quality of healthcare. The main objective of the work was to create an IoT-based automated system using machine learning models for symptom-based COVID-19 prognosis.Methods:
Comparative analysis of predictive microbiology of COVID-19 from case symptoms using various machine learning classifiers like logistics regression, k-nearest neighbor, support vector machine, random forest, decision trees, Naïve Bayes, and gradient booster is reported here. For the sake of the validation and verification of the models, performance of each model based on the retrieved cloud-stored data was measured for accuracy.Results:
From the accuracy plot, it was concluded that k-NN was more accurate (97.97%) followed by decision tree (97.79), support vector machine (97.42), logistics regression (96.50), random forest (90.66), gradient boosting classifier (87.77), and Naïve Bayes (73.50) in COVID-19 prognosis.Conclusion:
The paper presents a health monitoring IoT framework having high clinical significance in real-time and remote healthcare monitoring. The findings reported here and the lessons learnt shall enable the healthcare system worldwide to counter not only this ongoing COVID but many other such global pandemics the humanity may suffer from time to come.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Transition to Adult Care
/
Internet of Things
/
COVID-19
Type of study:
Diagnostic study
/
Prognostic study
/
Randomized controlled trials
Topics:
Vaccines
Limits:
Humans
Language:
English
Journal:
Biomed Res Int
Year:
2022
Document Type:
Article
Affiliation country:
2022
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