• B Sai Satwika
• A Swamy
• D Pranitha
• G Pavan Kalyan
• C Mohan Rao

The heart muscle is injured when blood flow to the coronary artery is reduced or disrupted, resulting in coronary artery disease, often known as a heart attack. Without human assistance, hidden patterns can be found using machine learning. The suggested methodology intends to create an intelligent agent to detect any coronary heart disease well in advance of any unfavourable event. A dataset including around 70000 records consisting of 11 features is used. The model applies different feature selection techniques such as Pearson correlation & Information Gain Attribute Evaluator. This model is aimed to construct an ensemble method with classification algorithms- Naïve Bayes , Random Forest and Gradient Boosting. The presence or absence of the cardio vascular illness from the characteristics is also determined using the K Nearest Neighbor (KNN) and Support Vector Machine algorithms. AUC is a representation of separability's level or measurement. It demonstrates how well the model can distinguish between classes. The model's accuracy will be evaluated and enhanced.

Heart attacks, Cardio vascular diseases, Ensemble model, Classification, Pearson correlation.

"An Ensemble Approach to Predict the Presence of Cardio Vascular Disease using Machine Learning and Deep Learning.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 10, page no.c559-c563, October-2022, Available :http://www.jetir.org/papers/JETIR2210272.pdf

"An Ensemble Approach to Predict the Presence of Cardio Vascular Disease using Machine Learning and Deep Learning.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 10, page no. ppc559-c563, October-2022, Available at : http://www.jetir.org/papers/JETIR2210272.pdf