ArticleSensors (Basel, Switzerland)2022
Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application.
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 29 papers.
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Who cites it
29 citing papers in PubMed.
- Biomarkers in Disease Diagnosis and Monitoring: Insights into Clinical Applications and Mass Spectrometry-based Detection.Applied biochemistry and biotechnology · 2026Review
- Risk factors for severe COVID-19 and development of a predictive model.BMC pulmonary medicine · 2025Article
- 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
- Evaluation of Acute Pancreatitis Severity and Prognosis Using the Aggregate Systemic Inflammation Index (AISI) as a New Marker: A Comparison with Other Inflammatory Indices.Journal of clinical medicine · 2025Article
- A multicenter study on developing a prognostic model for severe fever with thrombocytopenia syndrome using machine learning.Frontiers in microbiology · 2025Article
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- 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
- Bioinformatics in Russia: history and present-day landscape.Briefings in bioinformatics · 2024Review
- 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
- Association Between Hemoglobin-Albumin-Lymphocyte-Platelet Index and Mortality in Hospitalized COVID-19 Omicron BA.2 Infected Patients.Infection and drug resistance · 2024Article
- Development of Biomarkers and Prognosis Model of Mortality Risk in Patients with COVID-19.Journal of inflammation research · 2024Article
- Prediction of early neurologic deterioration in patients with perforating artery territory infarction using machine learning: a retrospective study.Frontiers in neurology · 2024Article
- Applying Recurrent Neural Networks for Anomaly Detection in Electrocardiogram Sensor Data.Sensors (Basel, Switzerland) · 2023Article
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- Entropy-Based Machine Learning Model for Fast Diagnosis and Monitoring of Parkinson's Disease.Sensors (Basel, Switzerland) · 2023Article
- Artificial Neural Networks for IoT-Enabled Smart Applications: Recent Trends.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
Healthcare digitalization requires effective applications of human sensors, when various parameters of the human body are instantly monitored in everyday life due to the Internet of Things (IoT). In particular, machine learning (ML) sensors for the prompt diagnosis of COVID-19 are an important option for IoT application in healthcare and ambient assisted living (AAL). Determining a COVID-19 infected status with various diagnostic tests and imaging results is costly and time-consuming. This study provides a fast, reliable and cost-effective alternative tool for the diagnosis of COVID-19 based on the routine blood values (RBVs) measured at admission. The dataset of the study consists of a total of 5296 patients with the same number of negative and positive COVID-19 test results and 51 routine blood values. In this study, 13 popular classifier machine learning models and the LogNNet neural network model were exanimated. The most successful classifier model in terms of time and accuracy in the detection of the disease was the histogram-based gradient boosting (HGB) (accuracy: 100%, time: 6.39 sec). The HGB classifier identified the 11 most important features (LDL, cholesterol, HDL-C, MCHC, triglyceride, amylase, UA, LDH, CK-MB, ALP and MCH) to detect the disease with 100% accuracy. In addition, the importance of single, double and triple combinations of these features in the diagnosis of the disease was discussed. We propose to use these 11 features and their binary combinations as important biomarkers for ML sensors in the diagnosis of the disease, supporting edge computing on Arduino and cloud IoT service.
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