ArticleJournal of medical systems2016
Applying Data Mining Techniques to Improve Breast Cancer Diagnosis.
Article in Journal of medical systems, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Machine learning based classification of catastrophic health expenditures: a cross-sectional study of Korean low-income households.BMC health services research · 2025Article
- SEER-based machine learning prediction of bone metastasis in breast cancer: model development and validation.Gland surgery · 2025Article
- Deeper insight into speech characteristics of patients at ultra-high risk using classification and explainability models.Frontiers in psychiatry · 2025Article
- Annotating publicly-available samples and studies using interpretable modeling of unstructured metadata.Briefings in bioinformatics · 2024Article
- Machine learning-enabled prediction of prolonged length of stay in hospital after surgery for tuberculosis spondylitis patients with unbalanced data: a novel approach using explainable artificial intelligence (XAI).European journal of medical research · 2024Article
- Data mining techniques in breast cancer diagnosis at the cellular-molecular level.Journal of cancer research and clinical oncology · 2023Article
- Automatic breast cancer diagnosis based on hybrid dimensionality reduction technique and ensemble classification.Journal of cancer research and clinical oncology · 2023Article
- An Ensemble of Transfer Learning Models for the Prediction of Skin Cancers with Conditional Generative Adversarial Networks.Diagnostics (Basel, Switzerland) · 2022Article
- An automated machine learning tool for breast cancer diagnosis for healthcare professionals.Health systems (Basingstoke, England) · 2022Article
- Quantum transfer learning for breast cancer detection.Quantum machine intelligence · 2022Article
- Mammography Image-Based Diagnosis of Breast Cancer Using Machine Learning: A Pilot Study.Sensors (Basel, Switzerland) · 2021Article
- Application of data mining in the provision of in-home medical care for patients with advanced cancer.Translational cancer research · 2021Article
- A decision support system for mammography reports interpretation.Health information science and systems · 2020Article
- Analysis of Consciousness Level Using Galvanic Skin Response during Therapeutic Effect.Journal of medical systems · 2020Article
- Predictive modeling of proliferative vitreoretinopathy using automated machine learning by ophthalmologists without coding experience.Scientific reports · 2020Article
- A Novel Internet of Things Framework Integrated with Real Time Monitoring for Intelligent Healthcare Environment.Journal of medical systems · 2019Article
- Fuzzy Expert System based on a Novel Hybrid Stem Cell (HSC) Algorithm for Classification of Micro Array Data.Journal of medical systems · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the field of breast cancer research, and more than ever, new computer aided diagnosis based systems have been developed aiming to reduce diagnostic tests false-positives. Within this work, we present a data mining based approach which might support oncologists in the process of breast cancer classification and diagnosis. The present study aims to compare two breast cancer datasets and find the best methods in predicting benign/malignant lesions, breast density classification, and even for finding identification (mass / microcalcification distinction). To carry out these tasks, two matrices of texture features extraction were implemented using Matlab, and classified using data mining algorithms, on WEKA. Results revealed good percentages of accuracy for each class: 89.3 to 64.7 % - benign/malignant; 75.8 to 78.3 % - dense/fatty tissue; 71.0 to 83.1 % - finding identification. Among the different tests classifiers, Naive Bayes was the best to identify masses texture, and Random Forests was the first or second best classifier for the majority of tested groups.
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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.