Breast Cancer Detection Using Machine Learning Techniques
Keywords:
Machine learning, KNN, PCA, Breast cancerAbstract
Breast cancer affects approximately 10% of women worldwide at some point in their lives and has emerged as one of the most feared and prevalent cancers among women. The main dilemma arises when cancer cannot be properly detected in its early stages. Machine learning has proven to play a vital role in diagnosing diseases such as cancers. Effective methods for classification and data recognition are particularly essential in the medical field.
In this project, classification techniques were employed using the PCA-KNN algorithm on the Wisconsin Breast Cancer Dataset. The main objective was to evaluate the accuracy of data classification concerning the efficiency and effectiveness of the PCA-KNN algorithm in terms of precision, recall, specificity, and the F1 score. The experimental results demonstrated an accuracy of up to 99%.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-sa/4.0/

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The authors retain the copyright and grant the right to publish in the magazine for the first time with the transfer of the commercial right to Tishreen University Journal for Research and Scientific Studies - Engineering Sciences Series
Under a CC BY- NC-SA 04 license that allows others to share the work with of the work's authorship and initial publication in this journal. Authors can use a copy of their articles in their scientific activity, and on their scientific websites, provided that the place of publication is indicted in Tishreen University Journal for Research and Scientific Studies - Engineering Sciences Series . The Readers have the right to send, print and subscribe to the initial version of the article, and the title of Tishreen University Journal for Research and Scientific Studies - Engineering Sciences Series Publisher
journal uses a CC BY-NC-SA license which mean
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material
- The licensor cannot revoke these freedoms as long as you follow the license terms.
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- NonCommercial — You may not use the material for commercial purposes.
- ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.