ArticleIEEE internet of things journal2021
Realizing an Effective COVID-19 Diagnosis System Based on Machine Learning and IoT in Smart Hospital Environment.
Article in IEEE internet of things journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 1 of them a synthesis that pooled it.
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Who cites it
46 citing papers in PubMed, 1 synthesis or guideline pooled it, 203 citations in OpenAlex.
- AI-driven techniques for detection and mitigation of SARS-CoV-2 spread: a review, taxonomy, and trends.Clinical and experimental medicine · 2025Pooled it
- Interpretable machine learning analysis of the impact of tranexamic acid combined with antibiotics on renal function and its risk factors in hip replacement surgeries.Frontiers in surgery · 2026Article
- Transition Toward Smart Hospitals: A Scoping Review of Features, Technologies, and Challenges.Health science reports · 2025Review
- Development of a Conceptual Framework for Smart Hospitals: A Qualitative Study in Iran.Health science reports · 2025Article
- Multi-resource constrained elective surgical scheduling with Nash equilibrium toward smart hospitals.Scientific reports · 2025Article
- Energy optimization and plant comfort management in smart greenhouses using the artificial bee colony algorithm.Scientific reports · 2025Article
- Artificial Intelligence in Biomedical Sciences: A Scoping Review.British journal of biomedical science · 2025Article
- Clinical decision support systems (CDSS) in assistance to COVID-19 diagnosis: A scoping review on types and evaluation methods.Health science reports · 2024Article
- Lightweight Multi-Class Support Vector Machine-Based Medical Diagnosis System with Privacy Preservation.Sensors (Basel, Switzerland) · 2023Article
- Article
- A Survey of COVID-19 Diagnosis Using Routine Blood Tests with the Aid of Artificial Intelligence Techniques.Diagnostics (Basel, Switzerland) · 2023Review
- Simultaneous Super-Resolution and Classification of Lung Disease Scans.Diagnostics (Basel, Switzerland) · 2023Article
- Combating Covid-19 using machine learning and deep learning: Applications, challenges, and future perspectives.Array (New York, N.Y.) · 2023Review
- COVID-19 health data analysis and personal data preserving: A homomorphic privacy enforcement approach.Computer communications · 2023Article
- Combined Cloud-Based Inference System for the Classification of COVID-19 in CT-Scan and X-Ray Images.New generation computing · 2023Article
- Assessment of regional economic restorability under the stress of COVID-19 using the new interval type-2 fuzzy ORESTE method.Complex & intelligent systems · 2022Article
- Efficient analysis of COVID-19 clinical data using machine learning models.Medical & biological engineering & computing · 2022Article
- Artificial intelligence for forecasting and diagnosing COVID-19 pandemic: A focused review.Artificial intelligence in medicine · 2022Review
- QUCoughScope: An Intelligent Application to Detect COVID-19 Patients Using Cough and Breath Sounds.Diagnostics (Basel, Switzerland) · 2022Article
- Software system to predict the infection in COVID-19 patients using deep learning and web of things.Software: practice & experience · 2022Article
Corrections and comments
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Authors and funding
7 authors at 6 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.