A Comparative Study of Advanced Machine Learning Ensemble Techniques for Classification of Breast Cancer

Clicks: 4
ID: 312848
2024
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #98 of 705 articles by views in Journal of Computing & Biomedical Informatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 705 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
The most prevalent and the life-threatening diseases include Breast cancer worldwide. An early detection and diagnosis which are accurate is essential for improving in survival rates. In the real-time applications, models of machine learning provide powerful tools for aiding in medical professionals in diagnosing the breast cancer very accurately and efficiently. This research focuses on application of advanced ensemble techniques, such as the Random Forest, the Support Vector Machines (SVM), the K-Nearest Neighbors (KNN), the Stacking, the Boosting, and the Blending, to classify cancer of breast as either benign or malignant. After a comprehensive analysis, the Boosting emerged as the highest-performing model with an accurate precision of 0.8493 and a ROC AUC of 0.9051. These findings are align with the Sustainable Development Goal (SDG) 3, which advocates for health care and well-being, that highlighting the importance of an accessible, data-driven education in healthcare and decision of the support systems. Hence, our work suggests that these models can significantly enhance precision in diagnostic, reducing the burden on systems of healthcare, especially in underserved areas.
Reference Key
imported_1777057309_69ebbe1d1b5a1 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Tayyaba Yasmeen,Muazzam Ali,M U Hashmi,M Adnan Hashmi,Zeeshan Mehmood
Journal Journal of Computing & Biomedical Informatics
Year 2024
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/648
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.