ArticleBioData mining2021
Gaussian noise up-sampling is better suited than SMOTE and ADASYN for clinical decision making.
Article in BioData mining, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
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- A synthetic oversampling-based customized ResNet51-Conv1D framework for early colorectal cancer prediction using structured clinical data from the PLCO screening trial.BMC medical informatics and decision making · 2026Article
- Artificial intelligence for precision oncology from phenotyping and drug discovery to clinical translation.Discover oncology · 2026Review
- Early Prediction of Adverse Stroke Outcomes Using Nonclinical Factors and Missing Data: A Machine Learning Study.Cerebrovascular diseases (Basel, Switzerland) · 2026Article
- Neurotransmitter-based machine-learning model for distinguishing Alzheimer's disease and mild cognitive impairment.The Journal of international medical research · 2026Article
- Evaluation of Machine Learning Model Performance in Diabetic Foot Ulcer: Retrospective Cohort Study.JMIR medical informatics · 2025Article
- CLCluster: A redundancy-reduction contrastive learning-based clustering method of cancer subtype based on multi-omics data.Molecular therapy. Nucleic acids · 2025Article
- Large-Scale Comparative Analysis of Canine and Human Osteosarcomas Uncovers Conserved Clinically Relevant Tumor Microenvironment Subtypes.Clinical cancer research : an official journal of the American Association for Cancer Research · 2024Article
- An alternating multiple residual Wasserstein regularization model for Gaussian image denoising.Scientific reports · 2024Article
- Processing imbalanced medical data at the data level with assisted-reproduction data as an example.BioData mining · 2024Article
- Prediction of prostate cancer biochemical recurrence by using discretization supports the critical contribution of the extra-cellular matrix genes.Scientific reports · 2023Article
- Complexity and Entropy in Physiological Signals (CEPS): Resonance Breathing Rate Assessed Using Measures of Fractal Dimension, Heart Rate Asymmetry and Permutation Entropy.Entropy (Basel, Switzerland) · 2023Article
- AI-based multi-PRS models outperform classical single-PRS models.Frontiers in genetics · 2023Article
- Vision for Improving Pregnancy Health: Innovation and the Future of Pregnancy Research.Reproductive sciences (Thousand Oaks, Calif.) · 2022Review
- A flexible data-driven audiological patient stratification method for deriving auditory profiles.Frontiers in neurology · 2022Article
- Cost-Efficient Early Diagnostic Tool for Lung Cancer: Explainable AI in Clinical Systems.Technology in cancer research & treatmentArticle
- Machine learning prediction of coal workers' pneumoconiosis classification based on few-shot clinical data.Digital healthArticle
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Clinical data sets have very special properties and suffer from many caveats in machine learning. They typically show a high-class imbalance, have a small number of samples and a large number of parameters, and have missing values. While feature selection approaches and imputation techniques address the former problems, the class imbalance is typically addressed using augmentation techniques. However, these techniques have been developed for big data analytics, and their suitability for clinical data sets is unclear.This study analyzed different augmentation techniques for use in clinical data sets and subsequent employment of machine learning-based classification. It turns out that Gaussian Noise Up-Sampling (GNUS) is not always but generally, is as good as SMOTE and ADASYN and even outperform those on some datasets. However, it has also been shown that augmentation does not improve classification at all in some cases.
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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.