SynthesisJournal of cancer survivorship : research and practice2025
Machine learning (ML) techniques to predict breast cancer in imbalanced datasets: a systematic review.
Synthesis in Journal of cancer survivorship : research and practice, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Machine Learning and Explainable AI for Breast Cancer Patient Prioritization: An Intelligent Decision-Support Framework.Bioengineering (Basel, Switzerland) · 2026Article
- Manifold-guided SMOTified dual-channel conditional GAN improving highly-imbalanced biomedical data classification.Discover oncology · 2026Article
- Recent Advances and Emerging Directions in Machine Learning-Based Breast Cancer Drug Discovery: A Comprehensive Review.Breast cancer (Dove Medical Press) · 2026Review
- Multimodal breast cancer diagnosis using feature fusion and deep learning.Frontiers in medicine · 2026Article
- Multimodal fusion of ultrasound images using HXM net for breast cancer diagnosis.Scientific reports · 2025Article
- A Hybrid Ensemble Learning Framework for Predicting Lumbar Disc Herniation Recurrence: Integrating Supervised Models, Anomaly Detection, and Threshold Optimization.Diagnostics (Basel, Switzerland) · 2025Article
- Exploring Task-Related EEG for Cross-Subject Early Alzheimer's Disease Susceptibility Prediction in Middle-Aged Adults Using Multitaper Spectral Analysis.Sensors (Basel, Switzerland) · 2024Article
- Predicting the Risk of Loneliness in Children and Adolescents: A Machine Learning Study.Behavioral sciences (Basel, Switzerland) · 2024Article
- CASCADE: Context-Aware Data-Driven AI for Streamlined Multidisciplinary Tumor Board Recommendations in Oncology.Cancers · 2024Article
- Transparent deep learning to identify autism spectrum disorders (ASD) in EHR using clinical notes.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Histopathology in focus: a review on explainable multi-modal approaches for breast cancer diagnosis.Frontiers in medicine · 2024Review
- Predicting cancer treatment outcomes using machine learning enabled clinical decision support systems.Digital healthReview
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Knowledge discovery in databases (KDD) is crucial in analyzing data to extract valuable insights. In medical outcome prediction, KDD is increasingly applied, particularly in diseases with high incidence, mortality, and costs, like cancer. ML techniques can develop more accurate predictive models for cancer patients' clinical outcomes, aiding informed healthcare decision-making. However, cancer prediction modeling faces challenges because of the unbalanced nature of the datasets, where there is a small minority category of patients with a cancer diagnosis compared to a majority category of cancer-free patients. Imbalanced datasets pose statistical hurdles like bias and overfitting when developing accurate prediction models. This systematic review focuses on breast cancer prediction articles published from 2008 to 2023. The objective is to examine ML methods used in three critical steps of KDD: preprocessing, data mining, and interpretation which address the imbalanced data problem in breast cancer prediction. This work synthesizes prior research in ML methods for breast cancer prediction. The findings help identify effective preprocessing strategies, including balancing and feature selection methods, robust predictive models, and evaluation metrics of those models. The study aims to inform healthcare providers and researchers about effective techniques for accurate breast cancer prediction.
Indexed as
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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.