ArticleJournal of healthcare engineering2019
Automated Breast Cancer Diagnosis Based on Machine Learning Algorithms.
Article in Journal of healthcare engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning and Natural Language Processing in Mental Health: Systematic Review.Journal of medical Internet research · 2021Pooled it
- HyFusion-X: hybrid deep and traditional feature fusion with ensemble classifiers for breast cancer detection using mammogram and ultrasound images.Scientific reports · 2025Article
- A simple and fast explainable artificial intelligence-based pre-screening tool for breast cancer tumor malignancy detection.Scientific reports · 2025Article
- A quantum-optimized approach for breast cancer detection using SqueezeNet-SVM.Scientific reports · 2025Article
- Application of an improved LightGBM hybrid integration model combining gradient harmonization and Jacobian regularization for breast cancer diagnosis.Scientific reports · 2025Article
- Advances in breast cancer diagnosis: a comprehensive review of imaging, biosensors, and emerging wearable technologies.Frontiers in oncology · 2025Review
- Health prediction for king salmon via evolutionary machine learning with genetic programming.Journal of the Royal Society of New Zealand · 2025Article
- Emerging research trends in artificial intelligence for cancer diagnostic systems: A comprehensive review.Heliyon · 2024Review
- Breast cancer detection employing stacked ensemble model with convolutional features.Cancer biomarkers : section A of Disease markers · 2024Article
- Comparative Analysis of Data Preprocessing Methods in Machine Learning for Breast Cancer Classification.Proceedings. International Conference on Advanced Information Networking and Applications · 2024Article
- Usefulness of machine learning and deep learning approaches in screening and early detection of breast cancer.Heliyon · 2023Review
- Machine learning-based models for the prediction of breast cancer recurrence risk.BMC medical informatics and decision making · 2023Article
- Breast cancer diagnosis using the fast learning network algorithm.Frontiers in oncology · 2023Article
- Cloud Computing-Based Framework for Breast Tumor Image Classification Using Fusion of AlexNet and GLCM Texture Features with Ensemble Multi-Kernel Support Vector Machine (MK-SVM).Computational intelligence and neuroscience · 2022Article
- Comparative Analysis of Machine Learning Methods for Breast Cancer Classification in Genetic Sequences.Journal of environmental and public health · 2022Article
- Analysis of Signs and Effects of Surgical Breast Cancer Patients Based on Big Data Technology.Computational intelligence and neuroscience · 2022Article
- Developing the breast cancer risk prediction system using hybrid machine learning algorithms.Journal of education and health promotion · 2022Article
- Artificial intelligence assists precision medicine in cancer treatment.Frontiers in oncology · 2022Review
- Predicting Breast Cancer Based on Optimized Deep Learning Approach.Computational intelligence and neuroscience · 2022Article
- Optimal Deep Learning Enabled Prostate Cancer Detection Using Microarray Gene Expression.Journal of healthcare engineering · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There have been several empirical studies addressing breast cancer using machine learning and soft computing techniques. Many claim that their algorithms are faster, easier, or more accurate than others are. This study is based on genetic programming and machine learning algorithms that aim to construct a system to accurately differentiate between benign and malignant breast tumors. The aim of this study was to optimize the learning algorithm. In this context, we applied the genetic programming technique to select the best features and perfect parameter values of the machine learning classifiers. The performance of the proposed method was based on sensitivity, specificity, precision, accuracy, and the roc curves. The present study proves that genetic programming can automatically find the best model by combining feature preprocessing methods and classifier algorithms.
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.