ArticleBioData mining2023
A self-inspected adaptive SMOTE algorithm (SASMOTE) for highly imbalanced data classification in healthcare.
Article in BioData mining, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Cross-Model Explainability Consistency in Hepatitis C Stage Classification: A SHAP, LIME, and Counterfactual Analysis Across Five Machine Learning Architectures.Diagnostics (Basel, Switzerland) · 2026Article
- Single-label and multi-label classification for disease recognition with special consideration of comorbidities.BMC medical research methodology · 2026Article
- Integrating artificial intelligence and multi-omics data for precision oncology in endometrial cancer: a narrative review.Functional & integrative genomics · 2026Review
- Interpretable Semi-federated Learning for Multimodal Cardiac Imaging and Risk Stratification: A Privacy-Preserving Framework.Journal of imaging informatics in medicine · 2026Article
- A hybrid SMOTE and Gaussian mixture model based optimized XGBoost framework for bipolar disorder detection.Scientific reports · 2026Article
- Development and external validation of an explainable machine learning model for in-hospital mortality risk stratification in intensive care unit patients with heart failure.Frontiers in cardiovascular medicine · 2026Article
- Risk warning model for predicting sleep disorders in healthcare workers on long-term shifts.Sleep and biological rhythms · 2025Article
- Bio inspired feature selection and graph learning for sepsis risk stratification.Scientific reports · 2025Article
- Prediction of high-risk pregnancy based on machine learning algorithms.Scientific reports · 2025Article
- Harnessing Risk Assessment for Thrombosis and Bleeding to Optimize Anticoagulation Strategy in Nonvalvular Atrial Fibrillation.Thrombosis and haemostasis · 2025Article
- Reliable ECG Anomaly Detection on Edge Devices for Internet of Medical Things Applications.Sensors (Basel, Switzerland) · 2025Article
- Machine learning models predict coagulopathy in traumatic brain injury patients in ER.Frontiers in neurology · 2025Article
- A Differential Evolution-Based Optimized Ensemble for Balanced and Imbalanced Medical Datasets.F1000Research · 2025Article
- User-avatar bond as diagnostic indicator for gaming disorder: A word on the side of caution.Journal of behavioral addictions · 2024Article
- Prediction of prolonged mechanical ventilation in the intensive care unit via machine learning: a COVID-19 perspective.Scientific reports · 2024Article
- Processing imbalanced medical data at the data level with assisted-reproduction data as an example.BioData mining · 2024Article
- Recent Advances in Predictive Modeling with Electronic Health Records.IJCAI : proceedings of the conference · 2024Article
- Interpretable machine learning framework to predict gout associated with dietary fiber and triglyceride-glucose index.Nutrition & metabolism · 2024Article
- CT-based radiomics and clinical characteristics for predicting bone metastasis in lung adenocarcinoma patients.Translational lung cancer research · 2024Article
- Predictive modeling for postoperative delirium in elderly patients with abdominal malignancies using synthetic minority oversampling technique.World journal of gastrointestinal oncology · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
In many healthcare applications, datasets for classification may be highly imbalanced due to the rare occurrence of target events such as disease onset. The SMOTE (Synthetic Minority Over-sampling Technique) algorithm has been developed as an effective resampling method for imbalanced data classification by oversampling samples from the minority class. However, samples generated by SMOTE may be ambiguous, low-quality and non-separable with the majority class. To enhance the quality of generated samples, we proposed a novel self-inspected adaptive SMOTE (SASMOTE) model that leverages an adaptive nearest neighborhood selection algorithm to identify the "visible" nearest neighbors, which are used to generate samples likely to fall into the minority class. To further enhance the quality of the generated samples, an uncertainty elimination via self-inspection approach is introduced in the proposed SASMOTE model. Its objective is to filter out the generated samples that are highly uncertain and inseparable with the majority class. The effectiveness of the proposed algorithm is compared with existing SMOTE-based algorithms and demonstrated through two real-world case studies in healthcare, including risk gene discovery and fatal congenital heart disease prediction. By generating the higher quality synthetic samples, the proposed algorithm is able to help achieve better prediction performance (in terms of F1 score) on average compared to the other methods, which is promising to enhance the usability of machine learning models on highly imbalanced healthcare data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.