ArticleHealthcare (Basel, Switzerland)2020
A Comparative Analysis of Breast Cancer Detection and Diagnosis Using Data Visualization and Machine Learning Applications.
Article in Healthcare (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Independent evaluation of machine learning and deep learning models for breast cancer detection.PLOS digital health · 2026Article
- 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
- An Intelligent Hybrid Ensemble Model for Early Detection of Breast Cancer in Multidisciplinary Healthcare Systems.Diagnostics (Basel, Switzerland) · 2026Article
- Circulating miRNA as diagnostic tools for gynecological diseases and their applications in biosensor development.Analytical and bioanalytical chemistry · 2026Review
- Enhancing breast cancer diagnosis through machine learning algorithms.Scientific reports · 2025Article
- Machine learning-based hybrid risk estimation system (ERES) in cardiac surgery: Supplementary insights from the ASA score analysis.PLOS digital health · 2025Article
- A novel aggregated coefficient ranking based feature selection strategy for enhancing the diagnosis of breast cancer classification using machine learning.Scientific reports · 2025Article
- Enhancing breast cancer prediction through stacking ensemble and deep learning integration.PeerJ. Computer science · 2025Article
- Machine learning-based prediction of distant metastasis risk in invasive ductal carcinoma of the breast.PloS one · 2025Article
- Divulging Patterns: An Analytical Review for Machine Learning Methodologies for Breast Cancer Detection.Journal of Cancer · 2025Review
- Early detection and analysis of accurate breast cancer for improved diagnosis using deep supervised learning for enhanced patient outcomes.PeerJ. Computer science · 2025Article
- Brain tumor classification utilizing pixel distribution and spatial dependencies higher-order statistical measurements through explainable ML models.Scientific reports · 2024Article
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- Predictive modeling for breast cancer classification in the context of Bangladeshi patients by use of machine learning approach with explainable AI.Scientific reports · 2024Article
- Breast cancer detection employing stacked ensemble model with convolutional features.Cancer biomarkers : section A of Disease markers · 2024Article
- DMAeEDNet: Dense Multiplicative Attention Enhanced Encoder Decoder Network for Ultrasound-Based Automated Breast Lesion Segmentation.IEEE access : practical innovations, open solutions · 2024Article
- Influence of distance to health facilities on clinical breast cancer screening behaviour among women in five sub-Saharan African countries.BMC public health · 2023Article
- AI-Powered Diagnosis of Skin Cancer: A Contemporary Review, Open Challenges and Future Research Directions.Cancers · 2023Review
- Convolution Neural Network for Breast Cancer Detection and Classification Using Deep Learning.Asian Pacific journal of cancer prevention : APJCP · 2023Article
Corrections and comments
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1 author.
Funding
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Abstract
In the developing world, cancer death is one of the major problems for humankind. Even though there are many ways to prevent it before happening, some cancer types still do not have any treatment. One of the most common cancer types is breast cancer, and early diagnosis is the most important thing in its treatment. Accurate diagnosis is one of the most important processes in breast cancer treatment. In the literature, there are many studies about predicting the type of breast tumors. In this research paper, data about breast cancer tumors from Dr. William H. Walberg of the University of Wisconsin Hospital were used for making predictions on breast tumor types. Data visualization and machine learning techniques including logistic regression, k-nearest neighbors, support vector machine, naïve Bayes, decision tree, random forest, and rotation forest were applied to this dataset. R, Minitab, and Python were chosen to be applied to these machine learning techniques and visualization. The paper aimed to make a comparative analysis using data visualization and machine learning applications for breast cancer detection and diagnosis. Diagnostic performances of applications were comparable for detecting breast cancers. Data visualization and machine learning techniques can provide significant benefits and impact cancer detection in the decision-making process. In this paper, different machine learning and data mining techniques for the detection of breast cancer were proposed. Results obtained with the logistic regression model with all features included showed the highest classification accuracy (98.1%), and the proposed approach revealed the enhancement in accuracy performances. These results indicated the potential to open new opportunities in the detection of breast cancer.
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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.