ArticleJournal of biomedical physics & engineering2022
Prediction of Breast Cancer using Machine Learning Approaches.
Article in Journal of biomedical physics & engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Deep-learning-based breast cancer stage prediction from H&E-stained whole-slide images in resource-constrained settings.Journal of pathology informatics · 2026Article
- A comparative analysis of data-driven models for breast cancer survival prediction.Scientific reports · 2026Article
- Machine Learning-Based Prediction of Breast Cancer in Women: Insights From Feature Selection of Clinical and Lifestyle Data.International journal of breast cancer · 2026Article
- Multimodal feature fusion model for breast mass malignant risk stratification.Frontiers in oncology · 2026Article
- Presenting a conceptual model for decision support systems in infertility: A developmental study.International journal of reproductive biomedicine · 2025Article
- Artificial Intelligence-Driven Personalization in Breast Cancer Screening: From Population Models to Individualized Protocols.Cancers · 2025Review
- A comparative analysis of parametric survival models and machine learning methods in breast cancer prognosis.Scientific reports · 2025Article
- Bayesian Model Prediction for Breast Cancer Survival: A Retrospective Analysis.European journal of breast health · 2025Article
- Breast Cancer Detection Using Convolutional Neural Networks: A Deep Learning-Based Approach.Cureus · 2025Review
- Enhanced and Interpretable Prediction of Multiple Cancer Types Using a Stacking Ensemble Approach with SHAP Analysis.Bioengineering (Basel, Switzerland) · 2025Article
- Image Analysis as tool for Predicting Colorectal Cancer Molecular Alterations: A Scoping Review.Molecular imaging and radionuclide therapy · 2025Article
- A novel aggregated coefficient ranking based feature selection strategy for enhancing the diagnosis of breast cancer classification using machine learning.Scientific reports · 2025Article
- Predicting deep vein thrombosis using machine learning and blood routine analysis.Frontiers in big data · 2025Article
- The role of artificial intelligence in enhancing breast cancer screening and diagnosis: A review of current advances.BioImpacts : BI · 2025Review
- Machine learning-based classification model to differentiate subtypes of invasive breast cancer using MRI.Frontiers in oncology · 2025Article
- Machine learning in predicting infertility treatment success: A systematic literature review of techniques.Journal of education and health promotion · 2025Review
- Metabolomics-Based Machine Learning Models Accurately Predict Breast Cancer Estrogen Receptor Status.International journal of molecular sciences · 2024Article
- An open codebase for enhancing transparency in deep learning-based breast cancer diagnosis utilizing CBIS-DDSM data.Scientific reports · 2024Article
- Prognostic significance of exportin-5 in hepatocellular carcinoma.World journal of gastrointestinal oncology · 2024Article
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Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Breast cancer is considered one of the most common cancers in women caused by various clinical, lifestyle, social, and economic factors. Machine learning has the potential to predict breast cancer based on features hidden in data. Objective: This study aimed to predict breast cancer using different machine-learning approaches applying demographic, laboratory, and mammographic data. Material and Methods: In this analytical study, the database, including 5,178 independent records, 25% of which belonged to breast cancer patients with 24 attributes in each record was obtained from Motamed cancer institute (ACECR), Tehran, Iran. The database contained 5,178 independent records, 25% of which belonged to breast cancer patients containing 24 attributes in each record. The random forest (RF), neural network (MLP), gradient boosting trees (GBT), and genetic algorithms (GA) were used in this study. Models were initially trained with demographic and laboratory features (20 features). The models were then trained with all demographic, laboratory, and mammographic features (24 features) to measure the effectiveness of mammography features in predicting breast cancer. Results: RF presented higher performance compared to other techniques (accuracy 80%, sensitivity 95%, specificity 80%, and the area under the curve (AUC) 0.56). Gradient boosting (AUC=0.59) showed a stronger performance compared to the neural network. Conclusion: Combining multiple risk factors in modeling for breast cancer prediction could help the early diagnosis of the disease with necessary care plans. Collection, storage, and management of different data and intelligent systems based on multiple factors for predicting breast cancer are effective in disease management.
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