Enhancing Breast Cancer Diagnosis with Integrated Dimensionality Reduction and Machine Learning Techniques

Clicks: 1
ID: 312952
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

Ranked #576 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
Breast cancer remains a significant cause of cancer-related mortality worldwide, highlighting the critical need for advancements in diagnostic techniques. Recent diagnostic methods, while effective, often face limitations in accuracy and efficiency. This paper aims to differentiate between tumorous (malignant) and non-tumorous (benign) cases of breast cancer using three publicly available datasets: Wisconsin Breast Cancer (WBC), Wisconsin Diagnostic Breast Cancer (WDBC), and Wisconsin Prognostic Breast Cancer (WPBC) datasets. We applied popular supervised machine learning classifiers, including Multi-layer Perceptron (MLP), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Naïve Bayes (NB), and K-Nearest Neighbor (KNN), in combination with dimensionality reduction techniques such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Factor Analysis (FA). The classifiers were evaluated based on accuracy, precision, recall and F1 score. The results show that, due to FA's emphasis on feature selection and noise reduction, the SVM with FA achieved the highest accuracy of 98.64% on the WBC dataset. MLP without any dimensionality reduction performed best with an accuracy of 98.26% on WDBC. Conversely, MLP and SVM with LDA yield an accuracy of 89.80% on the more complex and noisy WPBC dataset.
Reference Key
imported_1777058031_69ebc0ef10bdd Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Muhammad Abu Bakr
Journal Journal of Computing & Biomedical Informatics
Year 2024
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/573
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.