Enhancing Breast Cancer Diagnosis with Multivariate Bayesian PCA Model using  Machine learning

P. Srivyshnavi, M Darshan Teja  ,P Shankaraiah , Sreenivasulu T

Enhancing Breast Cancer Diagnosis with Multivariate Bayesian PCA Model using Machine learning

Authors

P. Srivyshnavi, M Darshan Teja ,P Shankaraiah , Sreenivasulu T

Keywords:

Breast Cancer Diagnosis, MBPCA, GNB, Accuracy, Attribute Insights.

Abstract

This research presents a multivariate Bayesian PCA model as a potential method for more accurately diagnosing breast cancer. The model uses a comprehensive dataset that includes attributes such as "radius", "texture", "perimeter", and "area" across dimensions (including "mean," "SE," and "worst") in order to forecast whether or not the outcomes will be "malignant" or "benign.

References

Nounou MN, Bakshi BR, Goel PK, Shen X. Bayesian principal component analysis. Journal of Chemometrics: A Journal of the Chemometrics Society. 2002 Nov;16(11):576-95.

Tsehay Admassu Assegie SS. A support vector machine and decision tree based breast cancer prediction. International Journal of Engineering and Advanced Technology (IJEAT), ISSN. 2020 Feb:2249-8958.

Downloads

Published

2026-06-05

How to Cite

P. Srivyshnavi, M Darshan Teja ,P Shankaraiah , Sreenivasulu T. (2026). Enhancing Breast Cancer Diagnosis with Multivariate Bayesian PCA Model using Machine learning . Journal of Computational Analysis and Applications (JoCAAA), 31(4), 2951–2965. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/5530