ReviewBioMed research international2020
Tabu Search and Machine-Learning Classification of Benign and Malignant Proliferative Breast Lesions.
Review in BioMed research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 6 papers.
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
6 citing papers in PubMed.
- Divulging Patterns: An Analytical Review for Machine Learning Methodologies for Breast Cancer Detection.Journal of Cancer · 2025Review
- DF-dRVFL: A novel deep feature based classifier for breast mass classification.Multimedia tools and applications · 2024Article
- A quantitative heterogeneity analysis approach to molecular subtype recognition of breast cancer in dynamic contrast-enhanced magnetic imaging images from radiomics data.Quantitative imaging in medicine and surgery · 2023Article
- Effect of Intensive Psychological Care on Patients with Benign Breast Lumps after Mammotome-Assisted Tumor Resection.Evidence-based complementary and alternative medicine : eCAM · 2022Article
- Framework for classification of cancer gene expression data using Bayesian hyper-parameter optimization.Medical & biological engineering & computing · 2021Article
- Corrigendum to "Tabu Search and Machine-Learning Classification of Benign and Malignant Proliferative Breast Lesions".BioMed research international · 2020Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is the most diagnosed cancer among women around the world. The development of computer-aided diagnosis tools is essential to help pathologists to accurately interpret and discriminate between malignant and benign tumors. This paper proposes the development of an automated proliferative breast lesion diagnosis based on machine-learning algorithms. We used Tabu search to select the most significant features. The evaluation of the feature is based on the dependency degree of each attribute in the rough set. The categorization of reduced features was built using five machine-learning algorithms. The proposed models were applied to the BIDMC-MGH and Wisconsin Diagnostic Breast Cancer datasets. The performance measures of the used models were evaluated owing to five criteria. The top performing models were AdaBoost and logistic regression. Comparisons with others works prove the efficiency of the proposed method for superior diagnosis of breast cancer against the reviewed classification techniques.
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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.