ArticleSensors (Basel, Switzerland)2022
Diagnosis and Prognosis of COVID-19 Disease Using Routine Blood Values and LogNNet Neural Network.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Machine learning models predict long COVID outcomes based on baseline clinical and immunologic factors.Communications medicine · 2026Article
- Identification of right ventricular dysfunction with LogNNet based diagnostic model: A comparative study with supervised ML algorithms.Scientific reports · 2025Article
- The mean corpuscular volume (MCV) is a hematological biomarker associated with COVID-19 mortality risk.Biomarkers in medicine · 2025Article
- Article
- A multicenter study on developing a prognostic model for severe fever with thrombocytopenia syndrome using machine learning.Frontiers in microbiology · 2025Article
- The possibilities of prospective assessment of SARS-CoV-2 infection risk based on an artificial neural network model - a cross-sectional andArchives of medical science : AMS · 2025Article
- Single Cell Transcriptomics Genomics Based on Machine Learning Algorithm: Constructing and Validating Neutrophil Extracellular Trap Gene Model in COPD.International journal of general medicine · 2025Article
- Machine learning approaches to predict the need for intensive care unit admission among Iranian COVID-19 patients based on ICD-10: A cross-sectional study.Health science reports · 2024Article
- Presepsin as a prognostic biomarker in COVID-19 patients: combining clinical scoring systems and laboratory inflammatory markers for outcome prediction.Virology journal · 2024Article
- Association of Coagulopathy and Inflammatory Biomarkers with Severity in SARS-CoV-2-Infected Individuals of the Al-Qunfudhah Region of Saudi Arabia.Healthcare (Basel, Switzerland) · 2024Article
- Development of Biomarkers and Prognosis Model of Mortality Risk in Patients with COVID-19.Journal of inflammation research · 2024Article
- Article
- A Novel COVID-19 Diagnosis Approach Utilizing a Comprehensive Set of Diagnostic Information (CSDI).Journal of clinical medicine · 2023Article
- Hemoglobin and Its Z Score Reference Intervals in Febrile Children: A Cohort Study of 98,572 Febrile Children.Children (Basel, Switzerland) · 2023Article
- Artificial Neural Networks for IoT-Enabled Smart Applications: Recent Trends.Sensors (Basel, Switzerland) · 2023Article
- Common Prognostic Biomarkers and Outcomes in Patients with COVID-19 Infection in Saudi Arabia.Tropical medicine and infectious disease · 2023Article
- Article
- Ct,Future science OA · 2023Article
- Biomarkers: Promising and valuable tools towards diagnosis, prognosis and treatment of Covid-19 and other diseases.Heliyon · 2023Review
- Automatic Detection of Severely and Mildly Infected COVID-19 Patients with Supervised Machine Learning Models.Ingenierie et recherche biomedicale : IRBM = Biomedical engineering and research · 2023Article
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2 authors.
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Abstract
Since February 2020, the world has been engaged in an intense struggle with the COVID-19 disease, and health systems have come under tragic pressure as the disease turned into a pandemic. The aim of this study is to obtain the most effective routine blood values (RBV) in the diagnosis and prognosis of COVID-19 using a backward feature elimination algorithm for the LogNNet reservoir neural network. The first dataset in the study consists of a total of 5296 patients with the same number of negative and positive COVID-19 tests. The LogNNet-model achieved the accuracy rate of 99.5% in the diagnosis of the disease with 46 features and the accuracy of 99.17% with only mean corpuscular hemoglobin concentration, mean corpuscular hemoglobin, and activated partial prothrombin time. The second dataset consists of a total of 3899 patients with a diagnosis of COVID-19 who were treated in hospital, of which 203 were severe patients and 3696 were mild patients. The model reached the accuracy rate of 94.4% in determining the prognosis of the disease with 48 features and the accuracy of 82.7% with only erythrocyte sedimentation rate, neutrophil count, and C reactive protein features. Our method will reduce the negative pressures on the health sector and help doctors to understand the pathogenesis of COVID-19 using the key features. The method is promising to create mobile health monitoring systems in the Internet of Things.
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