A novel combined dynamic ensemble selection model for imbalanced data to detect COVID-19 from complete blood count

Wu, Jiachao; Shen, Jiang; Xu, Man; Shao, Ming

doi:10.1016/j.cmpb.2021.106444

Cited by 15 publications

(10 citation statements)

References 50 publications

(65 reference statements)

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“…In Wu et al [ 43 ], to identify COVID-19 from a complete blood count, a mixed dynamic ensemble selection (DES) approach for unbalanced data is suggested. This approach combines data preparation with enhanced DES.…”

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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“…Datasets related to COVID-19 have imbalanced data ( 62 ); some studies declare the improvement of machine learning methods applying SMOTE technique ( 63 – 66 ) and a novel variant of SMOTE ( 67 ), also ROSE is used ( 68 ).…”

Section: Methodsmentioning

confidence: 99%

Individual Factors Associated With COVID-19 Infection: A Machine Learning Study

Real

Martínez-García²,

Márquez³

et al. 2022

Front. Public Health

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The fast, exponential increase of COVID-19 infections and their catastrophic effects on patients' health have required the development of tools that support health systems in the quick and efficient diagnosis and prognosis of this disease. In this context, the present study aims to identify the potential factors associated with COVID-19 infections, applying machine learning techniques, particularly random forest, chi-squared, xgboost, and rpart for feature selection; ROSE and SMOTE were used as resampling methods due to the existence of class imbalance. Similarly, machine and deep learning algorithms such as support vector machines, C4.5, random forest, rpart, and deep neural networks were explored during the train/test phase to select the best prediction model. The dataset used in this study contains clinical data, anthropometric measurements, and other health parameters related to smoking habits, alcohol consumption, quality of sleep, physical activity, and health status during confinement due to the pandemic associated with COVID-19. The results showed that the XGBoost model got the best features associated with COVID-19 infection, and random forest approximated the best predictive model with a balanced accuracy of 90.41% using SMOTE as a resampling technique. The model with the best performance provides a tool to help prevent contracting SARS-CoV-2 since the variables with the highest risk factor are detected, and some of them are, to a certain extent controllable.

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“…Considering the data imbalance, the predictive performances of the four mod els were compared based on the three evaluation metrics, including F1, G-mean, and t J o u r n a l P r e -p r o o f he area under the receiver operating characteristic curve (AUROC), to select the optim al model. 32,33 Other evaluation metrics included confusion metrics, accuracy, precision, recall, and specificity. The confusion matrices included four indicators: true negative, f alse positive, false negative, and true positive 32 .…”

Section: Performance and Validation Of Modelsmentioning

confidence: 99%