ArticleJournal of medical systems2019
Breast Cancer Diagnosis Using Feature Ensemble Learning Based on Stacked Sparse Autoencoders and Softmax Regression.
Article in Journal of medical systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Role of urinary leukocytes in the risk stratification of prostate cancer using nonlinear stacking learning strategy: a bi-cohort diagnostic study.Frontiers in oncology · 2026Article
- Visual impairment prevention by early detection of diabetic retinopathy based on stacked auto-encoder.Scientific reports · 2025Article
- BCED-Net: Breast Cancer Ensemble Diagnosis Network using transfer learning and the XGBoost classifier with mammography images.Osong public health and research perspectives · 2024Article
- MENet: A Mitscherlich function based ensemble of CNN models to classify lung cancer using CT scans.PloS one · 2024Article
- A structured combination of ensemble classifier and filter-based feature selection to improve breast cancer diagnosis.Journal of cancer research and clinical oncology · 2023Article
- Review
- Machine learning based gray-level co-occurrence matrix early warning system enables accurate detection of colorectal cancer pelvic bone metastases on MRI.Frontiers in oncology · 2023Article
- ETECADx: Ensemble Self-Attention Transformer Encoder for Breast Cancer Diagnosis Using Full-Field Digital X-ray Breast Images.Diagnostics (Basel, Switzerland) · 2022Article
- Interpretable AI for bio-medical applications.Complex engineering systems (Alhambra, Calif.) · 2022Article
- Prediction of successful aging using ensemble machine learning algorithms.BMC medical informatics and decision making · 2022Article
- Review
- A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis.Archives of computational methods in engineering : state of the art reviews · 2022Review
- Real-time internet of medical things framework for early detection of Covid-19.Neural computing & applications · 2022Article
- Review
- A pathway-informed disease-related gene identification approach and its application to screen novel risk genes for Alzheimer's disease.Journal of Alzheimer's disease reportsArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nowadays, the most frequent cancer in women is breast cancer (malignant tumor). If breast cancer is detected at the beginning stage, it can often be cured. Many researchers proposed numerous methods for early prediction of this Cancer. In this paper, we proposed feature ensemble learning based on Sparse Autoencoders and Softmax Regression for classification of Breast Cancer into benign (non-cancerous) and malignant (cancerous). We used Breast Cancer Wisconsin (Diagnostic) medical data sets from the UCI machine learning repository. The proposed method is assessed using various performance indices like true classification accuracy, specificity, sensitivity, recall, precision, f measure, and MCC. Simulation and result proved that the proposed approach gives better results in terms of different parameters. The prediction results obtained by the proposed approach were very promising (98.60% true accuracy). In addition, the proposed method outperforms the Stacked Sparse Autoencoders and Softmax Regression based (SSAE-SM) model and other State-of-the-art classifiers in terms of various performance indices. Experimental simulations, empirical results, and statistical analyses are also showing that the proposed model is an efficient and beneficial model for classification of Breast Cancer. It is also comparable with the existing machine learning and soft computing approaches present in the related literature.
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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.