ArticleScientific reports2021
Research on expansion and classification of imbalanced data based on SMOTE algorithm.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 2 of them syntheses that pooled it.
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Who cites it
55 citing papers in PubMed, 2 syntheses or guidelines pooled it.
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- Machine learning (ML) techniques to predict breast cancer in imbalanced datasets: a systematic review.Journal of cancer survivorship : research and practice · 2025Pooled it
- [Prediction of the occurrence risk of progressive liver fibrosis in patients with metabolic-associated fatty liver disease based on the SMOTE algorithm and nomogram].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2026Article
- Determinants of adoption of climate-smart agriculture (CSA) practices in mushroom farming in Bangladesh.Scientific reports · 2026Article
- Development of a hemodynamic scoring system for acute pancreatitis: an exploratory approach to optimizing fluid resuscitation.BMC gastroenterology · 2026Article
- An adaptive data rebalancing framework for real-time traffic risk prediction.Scientific reports · 2026Article
- Predicting severe stunting and its determinants among under-five in Eastern African Countries: A machine learning algorithms.PloS one · 2026Article
- Preventing spread of the invasive spotted lanternfly via texture-based automated egg detection.Frontiers in insect science · 2026Article
- Effect of air pollution on clinical pregnancy outcomes following intrauterine insemination: a machine learning-based analysis.Frontiers in public health · 2026Article
- An explainable machine learning approach to predicting carbapenem resistance inFrontiers in cellular and infection microbiology · 2026Article
- Predicting Zygosity based on physical similarity of twin pairs with the aid of machine learning methods.BMC medical informatics and decision making · 2025Article
- Dynamic contrast sensitivity during human locomotion.Journal of vision · 2025Article
- Mechanism-Driven Features Enable Asn Deamidation Reactivity Prediction via Machine Learning Methods.Journal of chemical information and modeling · 2025Article
- FADEL: Ensemble Learning Enhanced by Feature Augmentation and Discretization.Bioengineering (Basel, Switzerland) · 2025Article
- Enhancing blockchain transaction classification with ensemble learning approaches.Scientific reports · 2025Article
- Deep learning neural network prediction of postoperative complications in patients undergoing laparoscopic right hemicolectomy with or without CME and CVL for colon cancer: insights from SICE (Società Italiana di Chirurgia Endoscopica) CoDIG data.Techniques in coloproctology · 2025Observational
- Integrating Machine Learning and Dynamic Digital Follow-up for Enhanced Prediction of Postoperative Complications in Bariatric Surgery.Obesity surgery · 2025Article
- A PSO weighted ensemble framework with SMOTE balancing for student dropout prediction in smart education systems.Scientific reports · 2025Article
- Supervised and unsupervised learning reveal heroin-induced impairments in astrocyte structural plasticity.Science advances · 2025Article
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Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the development of artificial intelligence, big data classification technology provides the advantageous help for the medicine auxiliary diagnosis research. While due to the different conditions in the different sample collection, the medical big data is often imbalanced. The class-imbalance problem has been reported as a serious obstacle to the classification performance of many standard learning algorithms. SMOTE algorithm could be used to generate sample points randomly to improve imbalance rate, but its application is affected by the marginalization generation and blindness of parameter selection. Focusing on this problem, an improved SMOTE algorithm based on Normal distribution is proposed in this paper, so that the new sample points are distributed closer to the center of the minority sample with a higher probability to avoid the marginalization of the expanded data. Experiments show that the classification effect is better when use proposed algorithm to expand the imbalanced dataset of Pima, WDBC, WPBC, Ionosphere and Breast-cancer-wisconsin than the original SMOTE algorithm. In addition, the parameter selection of the proposed algorithm is analyzed and it is found that the classification effect is the best when the distribution characteristics of the original data was maintained best by selecting appropriate parameters in our designed experiments.
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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.