COVID-19 diagnosis from routine blood tests using artificial intelligence techniques

Rikan, Samin Babaei; Azar, Amir Sorayaie; Ghafari, Ali; Mohasefi, Jamshid Bagherzadeh; Pirnejad, Habibollah

doi:10.1016/j.bspc.2021.103263

Cited by 44 publications

(39 citation statements)

References 37 publications

(101 reference statements)

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“…In Babaei Rikan et al [ 45 ], to diagnose positive instances of COVID-19 from three regular laboratory blood test datasets, seven ML, and four deep learning models were presented. To illustrate the relevance among samples, Pearson, Spearman, and Kendall correlation coefficients were used.…”

Section: Methodsmentioning

confidence: 99%

An Ensemble Learning Model for COVID-19 Detection from Blood Test Samples

Abayomi‐Alli

Damaševičius

Maskeliūnas

et al. 2022

Sensors

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Current research endeavors in the application of artificial intelligence (AI) methods in the diagnosis of the COVID-19 disease has proven indispensable with very promising results. Despite these promising results, there are still limitations in real-time detection of COVID-19 using reverse transcription polymerase chain reaction (RT-PCR) test data, such as limited datasets, imbalance classes, a high misclassification rate of models, and the need for specialized research in identifying the best features and thus improving prediction rates. This study aims to investigate and apply the ensemble learning approach to develop prediction models for effective detection of COVID-19 using routine laboratory blood test results. Hence, an ensemble machine learning-based COVID-19 detection system is presented, aiming to aid clinicians to diagnose this virus effectively. The experiment was conducted using custom convolutional neural network (CNN) models as a first-stage classifier and 15 supervised machine learning algorithms as a second-stage classifier: K-Nearest Neighbors, Support Vector Machine (Linear and RBF), Naive Bayes, Decision Tree, Random Forest, MultiLayer Perceptron, AdaBoost, ExtraTrees, Logistic Regression, Linear and Quadratic Discriminant Analysis (LDA/QDA), Passive, Ridge, and Stochastic Gradient Descent Classifier. Our findings show that an ensemble learning model based on DNN and ExtraTrees achieved a mean accuracy of 99.28% and area under curve (AUC) of 99.4%, while AdaBoost gave a mean accuracy of 99.28% and AUC of 98.8% on the San Raffaele Hospital dataset, respectively. The comparison of the proposed COVID-19 detection approach with other state-of-the-art approaches using the same dataset shows that the proposed method outperforms several other COVID-19 diagnostics methods.

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Section: Methodsmentioning

confidence: 99%

An Ensemble Learning Model for COVID-19 Detection from Blood Test Samples

Abayomi‐Alli

Damaševičius

Maskeliūnas

et al. 2022

Sensors

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“…PLT reflects a pathology risk/severity, for example, cancer [ 87 ]. [][ []4, 23, 26 []] [] [] [] [][ []28, 29, 30 []] [] [] [] [][ []31, 32, 33 []] [] [] [] [][ []35, 36, 37 []] [] [] [] [][ []38, 39, 40 []] [] [] [] [][ []42, 43, 44 []] [] [] [] [][ []45, 46, 47 []] [] [] [] [][ []50, 51, 52 []] [] [] [] [][ []23, 28 []] [] [] [] [][ []30, 38 []] [] [] [] [][ []39, 51 []] [] [] [] RBC Red blood cells Red blood cells are the most common type of blood cells that carry oxygen to the body tissues via blood flow through the circulatory system. Low/high RBC levels indicate health problems, for example liver disease.…”

Section: The Blood Test Informative Features Selectionmentioning

confidence: 99%

“…Low/high RBC levels indicate health problems, for example liver disease. 19 , 26 , 27 28 , 29 , 30 [] [] [] [][ []31, 33, 49 []] [] [] [] [][ []35, 36, 37 []] [] [] [] [][ []38, 42, 43 []] [] [] [] [][ []46, 47, 53 []] [] [] [] [][ []30, 35 []] [] [] [] LYM Lymphocytes White blood cells are a cellular component of the adaptive (specific) immune response: B-Lymphocytes produce antibodies; T-lymphocytes destruct the infected cells and regulate other leukocytes. Their concentration is low in severe COVID-19.…”

Section: The Blood Test Informative Features Selectionmentioning

confidence: 99%

“… [][ []4, 19, 26 []] [] [] [] [][ []28, 29, 30 []] [] [] [] [][ []31, 33, 34 []] [] [] [] [][ []35, 36, 38 []] [] [] [] [][ []39, 40, 41 []] [] [] [] [][ []42, 44, 45 []] [] [] [] 46 , 47 , 49 50 , 51 , 52 54 , 55 , 56 [ 57 ] [][ []34, 39 []] [] [] [] [][ []41, 44 []] [] [] [] [][ []50, 51 []] [] [] [] [][ []52, 55 []] [] [] [] [][ []56, 57 []] [] [] [] MCH Mean Corpuscular Hemoglobin The mean corpuscular hemoglobin is the average mass of HGB per a RBC in a blood sample. [][ []19, 26, 27 []] [] [] [] [][ []28, 29, 30 []] [] [] [] [][ []31, 32, 33 []] [] [] [] [][ []34, 35, 36 []] [] [] [] [][ []37, 46, 52 []] [] [] [], [ 53 ] MCHC Mean Corpuscular Hemoglobin Concentration The mean corpuscular hemoglobin concentration characterizes an average concentration of HGB in a RBC. MCHC also reflects the amount of hemoglobin per unit volume in a single red blood cell ...…”

Section: The Blood Test Informative Features Selectionmentioning

confidence: 99%

“…WBC are the cells of the immune system involved in protecting the body against infectious disease. [][ []4, 19, 26 []] [] [] [] [][ []27, 28, 29 []] [] [] [] [][ []30, 31, 32 []] [] [] [] [][ []33, 34, 35 []] [] [] [] [][ []36, 37, 38 []] [] [] [] [][ []39, 40, 41 []] [] [] [] [][ []42, 43, 44 []] [] [] [] [][ []45, 46, 47 []] [] [] [] [][ []50, 51, 52 []] [] [] [] [][ []53, 54, 55 []] [] [] [] [][ []56, 57 []] [] [] [] [4,26]] [28,30]] [38,39]] [51,52]] 53 ,56] [ 57 ] BAY Basophils Basophils are a type of white blood cells. Basophils have granules containing biologically active substances, with the help of which they regulate immune responses.…”

Section: The Blood Test Informative Features Selectionmentioning

confidence: 99%

See 2 more Smart Citations

Predictive models for COVID-19 detection using routine blood tests and machine learning

Kistenev

Vrazhnov

Шнайдер

et al. 2022

Heliyon

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Integrating routine blood biomarkers and artificial intelligence for supporting diagnosis of silicosis in engineered stone workers

Sanchez‐Morillo,

León‐Jiménez,

Guerrero‐Chanivet

et al. 2024

Bioengineering & Transla Med

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Engineered stone silicosis (ESS), primarily caused by inhaling respirable crystalline silica, poses a significant occupational health risk globally. ESS has no effective treatment and presents a rapid progression from simple silicosis (SS) to progressive massive fibrosis (PMF), with respiratory failure and death. Despite the use of diagnostic methods like chest x‐rays and high‐resolution computed tomography, early detection of silicosis remains challenging. Since routine blood tests have shown promise in detecting inflammatory markers associated with the disease, this study aims to assess whether routine blood biomarkers, coupled with machine learning techniques, can effectively differentiate between healthy individuals, subjects with SS, and PMF. To this end, 107 men diagnosed with silicosis, ex‐workers in the engineered stone (ES) sector, and 22 healthy male volunteers as controls not exposed to ES dust were recruited. Twenty‐one primary biochemical markers derived from peripheral blood extraction were obtained retrospectively from clinical hospital records. Relief‐F features selection technique was applied, and the resulting subset of 11 biomarkers was used to build five machine learning models, demonstrating high performance with sensitivities and specificities in the best case greater than 82% and 89%, respectively. The percentage of lymphocytes, the angiotensin‐converting enzyme, and lactate dehydrogenase indexes were revealed, among others, as blood biomarkers with significant cumulative importance for the machine learning models. Our study reveals that these biomarkers could detect a chronic inflammatory status and potentially serve as a supportive tool for the diagnosis, monitoring, and early detection of the progression of silicosis.

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COVID-19 diagnosis from routine blood tests using artificial intelligence techniques

Cited by 44 publications

References 37 publications

An Ensemble Learning Model for COVID-19 Detection from Blood Test Samples

An Ensemble Learning Model for COVID-19 Detection from Blood Test Samples

Predictive models for COVID-19 detection using routine blood tests and machine learning

Integrating routine blood biomarkers and artificial intelligence for supporting diagnosis of silicosis in engineered stone workers

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