• Harichselvam
• Jerald
• T V ananthan
• Harishwar J
• Maheswari A

Cardiovascular Diseases(CVDs) are the Leading global cause of mortality,driving te need for innovative early prediction methods. This study utilizes Machine Learning (ML) models , including Random Forest, XGBoost, and Logistic Regression, to predict CVD using a dataset of 70,000 patient records and 11 features. Ensemble learning via a Voting Classifier improved accuracy. Techniques like SMOTE addressed class imbalance, and feature scaling ensured performance. The Voting Classifier achieved the highest accuracy (74%), with XGBoost performing similarly. These results demonstrate ML’s potential in offering accurate, non-invasive tools for assessing cardiovascular risk.

Cardiovascular Disease Prediction,Machine learning , Ensemble Learning,XGboost,Random Forest,SMOTE

"Predicting Cardiovascular Disease Using Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 4, page no.a116-a123, April-2025, Available :http://www.jetir.org/papers/JETIR2504018.pdf

"Predicting Cardiovascular Disease Using Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 4, page no. ppa116-a123, April-2025, Available at : http://www.jetir.org/papers/JETIR2504018.pdf