ArticleCancers2024
Learning from Imbalanced Data: Integration of Advanced Resampling Techniques and Machine Learning Models for Enhanced Cancer Diagnosis and Prognosis.
Article in Cancers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed.
- Fusion prediction model for post-ERCP pancreatitis under NSAIDs prophylaxis.Surgical endoscopy · 2026Article
- Multimodal oral cancer detection with embedding-level oversampling of fused image and clinical data.Scientific reports · 2026Article
- Article
- Transfer Learning and Machine Learning for Training Five-Year Survival Prognostic Models in Early Breast Cancer: Development and Validation Study.Journal of medical Internet research · 2026Article
- Diffusion models vs. DCGANs for class-imbalanced lung cancer CT classification: A comparative study.Intelligence-based medicine · 2026Article
- Artificial intelligence for triple-negative breast cancer from imaging to multi-omics.Frontiers in oncology · 2026Review
- Optimized ensemble learning framework for neonatal asphyxia prediction using perinatal clinical features.Frontiers in public health · 2026Article
- Feasibility Assessment of Telehealth-Based Cancer Pain Management Through Machine Learning: A Prospective Clinical Study.Pain research & management · 2026Article
- Diffusion Models vs. DCGANs for Class-Imbalanced Lung Cancer CT Classification: A Comparative Study.bioRxiv : the preprint server for biology · 2025Article
- Comparative performance of bagging and boosting ensemble models for predicting lumpy skin disease with multiclass-imbalanced data.Scientific reports · 2025Article
- Dual Machine Learning Framework for Predicting Long-Term Glycemic Change and Prediabetes Risk in Young Taiwanese Men.Diagnostics (Basel, Switzerland) · 2025Article
- A systematic review of multiparametric magnetic resonance imaging radiomics in the prediction of brain invasion in meningioma.Translational cancer research · 2025Review
- Integrating tumor location into artificial intelligence-based prognostic models in cancer.World journal of clinical oncology · 2025Article
- Mitigating class imbalance in churn prediction with ensemble methods and SMOTE.Scientific reports · 2025Article
- Development and validation of machine learning models based on molecular features for estimating the probability of multiple primary lung carcinoma versus intrapulmonary metastasis in patients presenting multiple non-small cell lung cancers.Translational lung cancer research · 2025Article
- An Effective Methodology for Diabetes Prediction in the Case of Class Imbalance.Bioengineering (Basel, Switzerland) · 2025Article
- Advanced Brain Tumor Classification in MR Images Using Transfer Learning and Pre-Trained Deep CNN Models.Cancers · 2025Article
- Enhancing skin lesion classification: a CNN approach with human baseline comparison.PeerJ. Computer science · 2025Article
- An oversampling-undersampling strategy for large-scale data linkage.Frontiers in big data · 2025Article
- A Machine Learning Model for Predicting Breast Cancer Recurrence and Supporting Personalized Treatment Decisions Through Comprehensive Feature Selection and Explainable Ensemble Learning.Cancer management and research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
BACKGROUND/
objectivesThis study aims to evaluate the performance of various classification algorithms and resampling methods across multiple diagnostic and prognostic cancer datasets, addressing the challenges of class imbalance.
methodsA total of five datasets were analyzed, including three diagnostic datasets (Wisconsin Breast Cancer Database, Cancer Prediction Dataset, Lung Cancer Detection Dataset) and two prognostic datasets (Seer Breast Cancer Dataset, Differentiated Thyroid Cancer Recurrence Dataset). Nineteen resampling methods from three categories were employed, and ten classifiers from four distinct categories were utilized for comparison.
resultsThe results demonstrated that hybrid sampling methods, particularly SMOTEENN, achieved the highest mean performance at 98.19%, followed by IHT (97.20%) and RENN (96.48%). In terms of classifiers, Random Forest showed the best performance with a mean value of 94.69%, with Balanced Random Forest and XGBoost following closely. The baseline method (no resampling) yielded a significantly lower performance of 91.33%, highlighting the effectiveness of resampling techniques in improving model outcomes.
conclusionsThis research underscores the importance of resampling methods in enhancing classification performance on imbalanced datasets, providing valuable insights for researchers and healthcare professionals. The findings serve as a foundation for future studies aimed at integrating machine learning techniques in cancer diagnosis and prognosis, with recommendations for further research on hybrid models and clinical applications.
Indexed as
Identifiers
What OpenQuestion holds
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.