Improving Cardiovascular Disease Prediction Accuracy through Multi-Model Ensemble Learning Techniques

Authors:

Rajneesh Shrivastava ¹, Chandra Shekhar Gautam ²

^{*rajsp.shrivastava@gmail.com, **Shekharg84@gmail.com}

¹ CSE, AKS University Satna

² CSE, AKS University Satna

Abstract - The development of precise and effective predictive algorithms for early diagnosis is necessary because heart disease continues to be one of the world's leading causes of death. In order to increase the prediction accuracy of heart disease, this study suggests an optimized ensemble machine learning framework that combines several classification algorithms, such as K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), and Random Forest (RF). A benchmark dataset is first used to train and assess each model, and then performance metrics including accuracy, precision, recall, and F1-score are used to compare the models.

Key Words:  Heart Disease, Ensemble Learning, Machine Learning, KNN, SVM, Logistic Regression, Random Forest, Cleveland Dataset