ArticleComputational and mathematical methods in medicine2022
Predicting Breast Cancer Leveraging Supervised Machine Learning Techniques.
Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it, 48 citations in OpenAlex.
- Artificial Intelligence Pipeline for Mammography-Based Breast Cancer Detection: An Integrated Systematic Review and Large-Scale Experimental Validation.Medicina (Kaunas, Lithuania) · 2025Pooled it
- Development and performance of female breast cancer incidence risk prediction models: a systematic review and meta-analysis.Annals of medicine · 2025Pooled it
- Advanced deep learning and transfer learning approaches for breast cancer classification using advanced multi-line classifiers and datasets with model optimization and interpretability.PeerJ. Computer science · 2025Article
- Divulging Patterns: An Analytical Review for Machine Learning Methodologies for Breast Cancer Detection.Journal of Cancer · 2025Review
- Prediction model for ocular metastasis of breast cancer: machine learning model development and interpretation study.BMC cancer · 2024Article
- Prediction of childbearing tendency in women on the verge of marriage using machine learning techniques.Scientific reports · 2024Article
- Research on ultrasound-based radiomics: a bibliometric analysis.Quantitative imaging in medicine and surgery · 2024Article
- Enhancing lung cancer detection through hybrid features and machine learning hyperparameters optimization techniques.Heliyon · 2024Article
- Optimizing Skin Cancer Survival Prediction with Ensemble Techniques.Bioengineering (Basel, Switzerland) · 2023Article
- Imbalanced class distribution and performance evaluation metrics: A systematic review of prediction accuracy for determining model performance in healthcare systems.PLOS digital health · 2023Article
- Ensemble Learning for Breast Cancer Lesion Classification: A Pilot Validation Using Correlated Spectroscopic Imaging and Diffusion-Weighted Imaging.Metabolites · 2023Article
- Application of Artificial Intelligence in the Diagnosis, Treatment, and Prognostic Evaluation of Mediastinal Malignant Tumors.Journal of clinical medicine · 2023Review
- Enhancing breast ultrasound segmentation through fine-tuning and optimization techniques: Sharp attention UNet.PloS one · 2023Article
- Article
- A Novel CNN pooling layer for breast cancer segmentation and classification from thermograms.PloS one · 2022Article
- Breast Cancer Prediction Based on Multiple Machine Learning Algorithms.Technology in cancer research & treatmentArticle
Corrections and comments
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Authors and funding
9 authors at 7 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is one of the leading causes of increasing deaths in women worldwide. The complex nature (microcalcification and masses) of breast cancer cells makes it quite difficult for radiologists to diagnose it properly. Subsequently, various computer-aided diagnosis (CAD) systems have previously been developed and are being used to aid radiologists in the diagnosis of cancer cells. However, due to intrinsic risks associated with the delayed and/or incorrect diagnosis, it is indispensable to improve the developed diagnostic systems. In this regard, machine learning has recently been playing a potential role in the early and precise detection of breast cancer. This paper presents a new machine learning-based framework that utilizes the Random Forest, Gradient Boosting, Support Vector Machine, Artificial Neural Network, and Multilayer Perception approaches to efficiently predict breast cancer from the patient data. For this purpose, the Wisconsin Diagnostic Breast Cancer (WDBC) dataset has been utilized and classified using a hybrid Multilayer Perceptron Model (MLP) and 5-fold cross-validation framework as a working prototype. For the improved classification, a connection-based feature selection technique has been used that also eliminates the recursive features. The proposed framework has been validated on two separate datasets, i.e., the Wisconsin Prognostic dataset (WPBC) and Wisconsin Original Breast Cancer (WOBC) datasets. The results demonstrate improved accuracy of 99.12% due to efficient data preprocessing and feature selection applied to the input data.
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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.