ArticleBiomedical signal processing and control2022
COVID-19 diagnosis from routine blood tests using artificial intelligence techniques.
Article in Biomedical signal processing and control, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Predicting stroke severity of patients using interpretable machine learning algorithms.European journal of medical research · 2024Article
- Integrating routine blood biomarkers and artificial intelligence for supporting diagnosis of silicosis in engineered stone workers.Bioengineering & translational medicine · 2024Article
- Prediction equations for detecting COVID-19 infection using basic laboratory parameters.Journal of family medicine and primary care · 2024Article
- Comparing machine learning screening approaches using clinical data and cytokine profiles for COVID-19 in resource-limited and resource-abundant settings.Scientific reports · 2024Article
- Survival prediction of glioblastoma patients using modern deep learning and machine learning techniques.Scientific reports · 2024Article
- Monkeypox detection using deep neural networks.BMC infectious diseases · 2023Article
- A Survey of COVID-19 Diagnosis Using Routine Blood Tests with the Aid of Artificial Intelligence Techniques.Diagnostics (Basel, Switzerland) · 2023Review
- Clinical Hematochemical Parameters in Differential Diagnosis between Pediatric SARS-CoV-2 and Influenza Virus Infection: An Automated Machine Learning Approach.Children (Basel, Switzerland) · 2023Article
- Semi-Supervised KPCA-Based Monitoring Techniques for Detecting COVID-19 Infection through Blood Tests.Diagnostics (Basel, Switzerland) · 2023Article
- A Decision Support System for Diagnosis of COVID-19 from Non-COVID-19 Influenza-like Illness Using Explainable Artificial Intelligence.Bioengineering (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
- Raman spectroscopy and convolutional neural networks for monitoring biochemical radiation response in breast tumour xenografts.Scientific reports · 2023Article
- Article
- Application of machine learning techniques for predicting survival in ovarian cancer.BMC medical informatics and decision making · 2022Article
- Predictive models for COVID-19 detection using routine blood tests and machine learning.Heliyon · 2022Review
- Identification of characteristics frequency and hot-spots in protein sequence of COVID-19 disease.Biomedical signal processing and control · 2022Article
- An Ensemble Learning Model for COVID-19 Detection from Blood Test Samples.Sensors (Basel, Switzerland) · 2022Article
- A Novel Benchmark Dataset for COVID-19 Detection during Third Wave in Pakistan.Computational intelligence and neuroscience · 2022Article
- A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest.Informatics in medicine unlocked · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Coronavirus disease (COVID-19) is a unique worldwide pandemic. With new mutations of the virus with higher transmission rates, it is imperative to diagnose positive cases as quickly and accurately as possible. Therefore, a fast, accurate, and automatic system for COVID-19 diagnosis can be very useful for clinicians. In this study, seven machine learning and four deep learning models were presented to diagnose positive cases of COVID-19 from three routine laboratory blood tests datasets. Three correlation coefficient methods, i.e., Pearson, Spearman, and Kendall, were used to demonstrate the relevance among samples. A four-fold cross-validation method was used to train, validate, and test the proposed models. In all three datasets, the proposed deep neural network (DNN) model achieved the highest values of accuracy, precision, recall or sensitivity, specificity, F1-Score, AUC, and MCC. On average, accuracy 92.11%, specificity 84.56%, and AUC 92.20% values have been obtained in the first dataset. In the second dataset, on average, accuracy 93.16%, specificity 93.02%, and AUC 93.20% values have been obtained. Finally, in the third dataset, on average, the values of accuracy 92.5%, specificity 85%, and AUC 92.20% have been obtained. In this study, we used a statistical
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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.