ArticleJournal of King Saud University. Computer and information sciences2022
Pruning-based oversampling technique with smoothed bootstrap resampling for imbalanced clinical dataset of Covid-19.
Article in Journal of King Saud University. Computer and information sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Predicting 1-year successful clinical use of an arteriovenous access for hemodialysis using machine learning.NPJ digital medicine · 2025Article
- Predicting outcomes following endovascular aortoiliac revascularization using machine learning.NPJ digital medicine · 2025Article
- Predicting outcomes following open abdominal aortic aneurysm repair using machine learning.Scientific reports · 2025Article
- Using Machine Learning to Predict Outcomes Following Thoracic and Complex Endovascular Aortic Aneurysm Repair.Journal of the American Heart Association · 2025Article
- Using machine learning to predict outcomes following transcarotid artery revascularization.Scientific reports · 2025Article
- Predicting inferior vena cava filter complications using machine learning.Journal of vascular surgery. Venous and lymphatic disorders · 2024Article
- Using Machine Learning to Predict Outcomes Following Transfemoral Carotid Artery Stenting.Journal of the American Heart Association · 2024Article
- Predicting Outcomes Following Lower Extremity Endovascular Revascularization Using Machine Learning.Journal of the American Heart Association · 2024Article
- Machine Learning to Predict Outcomes of Endovascular Intervention for Patients With PAD.JAMA network open · 2024Article
- Predicting outcomes following lower extremity open revascularization using machine learning.Scientific reports · 2024Article
- Predicting Major Adverse Cardiovascular Events Following Carotid Endarterectomy Using Machine Learning.Journal of the American Heart Association · 2023Article
- Individual Factors Associated With COVID-19 Infection: A Machine Learning Study.Frontiers in public health · 2022Article
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2 authors.
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Abstract
The Coronavirus Disease (COVID-19) was declared a pandemic disease by the World Health Organization (WHO), and it has not ended so far. Since the infection rate of the COVID-19 increases, the computational approach is needed to predict patients infected with COVID-19 in order to speed up the diagnosis time and minimize human error compared to conventional diagnoses. However, the number of negative data that is higher than positive data can result in a data imbalance situation that affects the classification performance, resulting in a bias in the model evaluation results. This study proposes a new oversampling technique, i.e., TRIM-SBR, to generate the minor class data for diagnosing patients infected with COVID-19. It is still challenging to develop the oversampling technique due to the data's generalization issue. The proposed method is based on pruning by looking for specific minority areas while retaining data generalization, resulting in minority data seeds that serve as benchmarks in creating new synthesized data using bootstrap resampling techniques. Accuracy, Specificity, Sensitivity, F-measure, and AUC are used to evaluate classifier performance in data imbalance cases. The results show that the TRIM-SBR method provides the best performance compared to other oversampling techniques.
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