Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{180183,
        author = {Hitha Jain Y B and Naganand K Athreya and Harini R S and Sharath V and Archana VR and S Vinod Kumar},
        title = {Cardiovascular Disease Analysis and Prediction using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {262-269},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180183},
        abstract = {Cardiovascular disease (CVD) prediction 
using machine learning has gained momentum due to 
the availability of large clinical datasets. While existing 
literature extensively explores conventional classifiers 
such as Logistic Regression, Decision Tree, and 
Random Forest, this study investigates the impact of 
incorporating more advanced ensemble techniques, 
including Gradient Boosting Machine (GBM) and 
XGBoost, alongside detailed visual data diagnostics. 
The comparative performance evaluation highlights the 
benefits of integrating multiple classifiers and 
emphasizes the importance of data-driven insights in 
feature distribution and correlation. The study 
underscores the superiority of Logistic Regression for 
this dataset but also explores potential    improvements 
through ensemble learning},
        keywords = {},
        month = {May},
        }