ArticleSN computer science2022
Machine Learning-Based Research for COVID-19 Detection, Diagnosis, and Prediction: A Survey.
Article in SN computer science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Fairness Correction in COVID-19 Predictive Models Using Demographic Optimization: Algorithm Development and Validation Study.Online journal of public health informatics · 2026Article
- Credible capacity evaluation of virtual power plants considering wind and PV uncertainties.Scientific reports · 2025Article
- GenoDense-Net: unraveling the genomic puzzle of the global pathogen.Tropical diseases, travel medicine and vaccines · 2025Article
- Disease progression associated cytokines in COVID-19 patients with deteriorating and recovering health conditions.Scientific reports · 2024Article
- A novel hybrid supervised and unsupervised hierarchical ensemble for COVID-19 cases and mortality prediction.Scientific reports · 2024Article
- Real-time infectious disease endurance indicator system for scientific decisions using machine learning and rapid data processing.PeerJ. Computer science · 2024Article
- Virtual reality for assessing stereopsis performance and eye characteristics in Post-COVID.Scientific reports · 2023Article
- Application of Artificial Intelligence in Assessing the Self-Management Practices of Patients with Type 2 Diabetes.Healthcare (Basel, Switzerland) · 2023Article
- Classification of COVID-19 Patients into Clinically Relevant Subsets by a Novel Machine Learning Pipeline Using Transcriptomic Features.International journal of molecular sciences · 2023Article
- Role of different types of RNA molecules in the severity prediction of SARS-CoV-2 patients.Pathology, research and practice · 2023Article
- Cardiovascular and Renal Comorbidities Included into Neural Networks Predict the Outcome in COVID-19 Patients Admitted to an Intensive Care Unit: Three-Center, Cross-Validation, Age- and Sex-Matched Study.Journal of cardiovascular development and disease · 2023Article
- Generalized external synchronization of networks based on clustered pandemic systems-The approach of Covid-19 towards influenza.PloS one · 2023Article
- An overview of deep learning techniques for COVID-19 detection: methods, challenges, and future works.Multimedia systems · 2023Article
- Cloud-based COVID-19 disease prediction system from X-Ray images using convolutional neural network on smartphone.Multimedia tools and applications · 2022Article
- Time-Series Analysis and Healthcare Implications of COVID-19 Pandemic in Saudi Arabia.Healthcare (Basel, Switzerland) · 2022Article
- A Hybrid Feature Selection Approach to Screen a Novel Set of Blood Biomarkers for Early COVID-19 Mortality Prediction.Diagnostics (Basel, Switzerland) · 2022Article
- Artificial Intelligence and Deep Learning Assisted Rapid Diagnosis of COVID-19 from Chest Radiographical Images: A Survey.Contrast media & molecular imaging · 2022Review
- Authentication Schemes for Healthcare Applications Using Wireless Medical Sensor Networks: A Survey.SN computer science · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The year 2020 experienced an unprecedented pandemic called COVID-19, which impacted the whole world. The absence of treatment has motivated research in all fields to deal with it. In Computer Science, contributions mainly include the development of methods for the diagnosis, detection, and prediction of COVID-19 cases. Data science and Machine Learning (ML) are the most widely used techniques in this area. This paper presents an overview of more than 160 ML-based approaches developed to combat COVID-19. They come from various sources like Elsevier, Springer, ArXiv, MedRxiv, and IEEE Xplore. They are analyzed and classified into two categories: Supervised Learning-based approaches and Deep Learning-based ones. In each category, the employed ML algorithm is specified and a number of used parameters is given. The parameters set for each of the algorithms are gathered in different tables. They include the type of the addressed problem (detection, diagnosis, or detection), the type of the analyzed data (Text data, X-ray images, CT images, Time series, Clinical data,...) and the evaluated metrics (accuracy, precision, sensitivity, specificity, F1-Score, and AUC). The study discusses the collected information and provides a number of statistics drawing a picture about the state of the art. Results show that Deep Learning is used in 79% of cases where 65% of them are based on the Convolutional Neural Network (CNN) and 17% use Specialized CNN. On his side, supervised learning is found in only 16% of the reviewed approaches and only Random Forest, Support Vector Machine (SVM) and Regression algorithms are employed.
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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.