ArticleBMC medical informatics and decision making2022
Solving the class imbalance problem using ensemble algorithm: application of screening for aortic dissection.
Article in BMC medical informatics and decision making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
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- Deep learning in the early diagnosis of acute aortic dissection.Frontiers in cardiovascular medicine · 2026Review
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- Artificial intelligence for predicting 30-day mortality after surgery for femoral shaft fractures: A retrospective study.Indian journal of anaesthesia · 2025Article
- Automated Risser Grade Assessment of Pelvic Bones Using Deep Learning.Bioengineering (Basel, Switzerland) · 2025Article
- Advancing preeclampsia prediction: a tailored machine learning pipeline integrating resampling and ensemble models for handling imbalanced medical data.BioData mining · 2025Article
- Evaluating machine learning approaches for host prediction using H3 influenza genomic data.PloS one · 2025Article
- A Differential Evolution-Based Optimized Ensemble for Balanced and Imbalanced Medical Datasets.F1000Research · 2025Article
- Predictive Model for In-Hospital Death in Older Patients with Type 2 Diabetes Mellitus: A Multicenter Retrospective Study in Southwest China.Diabetes, metabolic syndrome and obesity : targets and therapy · 2025Article
- The gut microbiome in diabetic patients with hepatocellular carcinoma: distinct bacterial compositional shifts after hepatitis C virus eradication.Frontiers in microbiology · 2025Article
- Development and validation of the Florey Dementia Risk Score web-based tool to screen for Alzheimer's disease in primary care.EClinicalMedicine · 2024Article
- An ensemble model for predicting dispositions of emergency department patients.BMC medical informatics and decision making · 2024Article
- Modeling health risks using neural network ensembles.PloS one · 2024Article
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6 authors.
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Abstract
backgroundImbalance between positive and negative outcomes, a so-called class imbalance, is a problem generally found in medical data. Despite various studies, class imbalance has always been a difficult issue. The main objective of this study was to find an effective integrated approach to address the problems posed by class imbalance and to validate the method in an early screening model for a rare cardiovascular disease aortic dissection (AD).
methodsDifferent data-level methods, cost-sensitive learning, and the bagging method were combined to solve the problem of low sensitivity caused by the imbalance of two classes of data. First, feature selection was applied to select the most relevant features using statistical analysis, including significance test and logistic regression. Then, we assigned two different misclassification cost values for two classes, constructed weak classifiers based on the support vector machine (SVM) model, and integrated the weak classifiers with undersampling and bagging methods to build the final strong classifier. Due to the rarity of AD, the data imbalance was particularly prominent. Therefore, we applied our method to the construction of an early screening model for AD disease. Clinical data of 523,213 patients from the Institute of Hypertension, Xiangya Hospital, Central South University were used to verify the validity of this method. In these data, the sample ratio of AD patients to non-AD patients was 1:65, and each sample contained 71 features.
resultsThe proposed ensemble model achieved the highest sensitivity of 82.8%, with training time and specificity reaching 56.4 s and 71.9% respectively. Additionally, it obtained a small variance of sensitivity of 19.58 × 10
conclusionsIn this study, we demonstrate that the integration of feature selection, undersampling, cost-sensitive learning and bagging methods can overcome the challenge of class imbalance in a medical dataset and develop a practical screening model for AD, which could lead to a decision support for screening for AD at an early stage.
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