ArticleBMC medical informatics and decision making2022
A hybrid sampling algorithm combining synthetic minority over-sampling technique and edited nearest neighbor for missed abortion diagnosis.
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 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Sharing approaches in predictive genomics across animals, plants and humans.Nature genetics · 2026Review
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- A Novel Framework for Cardiovascular Disease Detection Using a Hybrid CWT-SIFT Image Representation and a Lightweight Residual Attention Network.Diagnostics (Basel, Switzerland) · 2025Article
- ProtoMAP: prototypical network based few-shot learning for missed abortion prediction.BMC medical informatics and decision making · 2025Article
- Explainable artificial intelligence for predicting medical students' performance in comprehensive assessments.Scientific reports · 2025Article
- Addressing data imbalance in collision risk prediction with active generative oversampling.Scientific reports · 2025Article
- Evaluating how different balancing data techniques impact on prediction of premature birth using machine learning models.PloS one · 2025Article
- The future of critical care: AI-powered mortality prediction for acute variceal gastrointestinal bleeding and acute non-variceal gastrointestinal bleeding patients.Frontiers in medicine · 2025Article
- Real-world data-driven early warning system for risk-stratified liver injury in hospitalized COVID-19 patients-Machine learning models for clinical decision support.Frontiers in public health · 2025Article
- Assessing the impact of digital health literacy on health management practices in Arab Middle Eastern and North African countries:Frontiers in digital health · 2025Article
- Hybrid intelligent systems for liver disease prediction: a demographic-aware machine learning framework.Frontiers in medicine · 2025Article
- TNFMolecular diversity · 2024Article
- Diabetes mellitus early warning and factor analysis using ensemble Bayesian networks with SMOTE-ENN and Boruta.Scientific reports · 2023Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundClinical diagnosis based on machine learning usually uses case samples as training samples, and uses machine learning to construct disease prediction models characterized by descriptive texts of clinical manifestations. However, the problem of sample imbalance often exists in the medical field, which leads to a decrease in classification performance of the machine learning.
methodsTo solve the problem of sample imbalance in medical dataset, we propose a hybrid sampling algorithm combining synthetic minority over-sampling technique (SMOTE) and edited nearest neighbor (ENN). Firstly, the SMOTE is used to over-sampling missed abortion and diabetes datasets, so that the number of samples of the two classes is balanced. Then, ENN is used to under-sampling the over-sampled dataset to delete the "noisy sample" in the majority. Finally, Random forest is used to model and predict the sampled missed abortion and diabetes datasets to achieve an accurate clinical diagnosis.
resultsExperimental results show that Random forest has the best classification performance on missed abortion and diabetes datasets after SMOTE-ENN sampled, and the MCC index is 95.6% and 90.0%, respectively. In addition, the results of pairwise comparison and multiple comparisons show that the SMOTE-ENN is significantly better than other sampling algorithms.
conclusionRandom forest has significantly improved all indexes on the missed abortion dataset after SMOTE-ENN sampled.
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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.