ArticleInternational journal of environmental research and public health2022
Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis.
Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predictive Modeling of Acute Respiratory Distress Syndrome Using Machine Learning: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Research progress of machine learning applications in gastric cancer diagnosis and therapy.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- 18F-FDG PET/CT-based deep radiomic models for enhancing chemotherapy response prediction in breast cancer.Medical oncology (Northwood, London, England) · 2025Article
- Advanced machine learning framework for enhancing breast cancer diagnostics through transcriptomic profiling.Discover oncology · 2025Article
- A novel aggregated coefficient ranking based feature selection strategy for enhancing the diagnosis of breast cancer classification using machine learning.Scientific reports · 2025Article
- Transfer Learning and Neural Network-Based Approach on Structural MRI Data for Prediction and Classification of Alzheimer's Disease.Diagnostics (Basel, Switzerland) · 2025Article
- Enhancing breast cancer prediction through stacking ensemble and deep learning integration.PeerJ. Computer science · 2025Article
- Divulging Patterns: An Analytical Review for Machine Learning Methodologies for Breast Cancer Detection.Journal of Cancer · 2025Review
- 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
- Development and interpretation of a machine learning predictive model for early cognitive impairment in hypertension associated with environmental factors.Frontiers in cardiovascular medicine · 2025Article
- Article
- Prediction model for ocular metastasis of breast cancer: machine learning model development and interpretation study.BMC cancer · 2024Article
- Advancements in Exosome Proteins for Breast Cancer Diagnosis and Detection: With a Focus on Nanotechnology.AAPS PharmSciTech · 2024Review
- Article
- Feature-based detection of breast cancer using convolutional neural network and feature engineering.Scientific reports · 2024Article
- Advancing Breast Cancer Diagnosis through Breast Mass Images, Machine Learning, and Regression Models.Sensors (Basel, Switzerland) · 2024Article
- Machine learning-based models for the prediction of breast cancer recurrence risk.BMC medical informatics and decision making · 2023Article
- Breast Cancer Classification Using Synthesized Deep Learning Model with Metaheuristic Optimization Algorithm.Diagnostics (Basel, Switzerland) · 2023Article
- A Catalogue of Machine Learning Algorithms for Healthcare Risk Predictions.Sensors (Basel, Switzerland) · 2022Article
- The Technology-Oriented Pathway for Auxiliary Diagnosis in the Digital Health Age: A Self-Adaptive Disease Prediction Model.International journal of environmental research and public health · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer death rates are higher than any other cancer in American women. Machine learning-based predictive models promise earlier detection techniques for breast cancer diagnosis. However, making an evaluation for models that efficiently diagnose cancer is still challenging. In this work, we proposed data exploratory techniques (DET) and developed four different predictive models to improve breast cancer diagnostic accuracy. Prior to models, four-layered essential DET, e.g., feature distribution, correlation, elimination, and hyperparameter optimization, were deep-dived to identify the robust feature classification into malignant and benign classes. These proposed techniques and classifiers were implemented on the Wisconsin Diagnostic Breast Cancer (WDBC) and Breast Cancer Coimbra Dataset (BCCD) datasets. Standard performance metrics, including confusion matrices and K-fold cross-validation techniques, were applied to assess each classifier's efficiency and training time. The models' diagnostic capability improved with our DET, i.e., polynomial SVM gained 99.3%, LR with 98.06%, KNN acquired 97.35%, and EC achieved 97.61% accuracy with the WDBC dataset. We also compared our significant results with previous studies in terms of accuracy. The implementation procedure and findings can guide physicians to adopt an effective model for a practical understanding and prognosis of breast cancer tumors.
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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.