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{191083,
        author = {Sunkara Srujan Bhargav and Polisetti Venkata Sarath Bhushan and Sutapalli Mukunda Raghuram and Atla Bhuvanika and Sonia Janyavula and Siddanathi Pavan Tarak and Pallempati Sri Harsha Vardhan},
        title = {Heart Disease Prediction Using Machine Learning and Ensemble Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {5689-5694},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191083},
        abstract = {This paper explores the development of a machine learning (ML) model for predicting the presence of heart disease based on clinical and demographic features. We preprocess the dataset, train various ML algorithms including logistic regression, decision trees, random forests, and K Nearest Neighbors (KNN), and evaluate their performance using metrics such as accuracy, precision, recall, and F1-score. In addition to these models, ensemble techniques like bagging, AdaBoost, gradient boosting, and stacking are also employed to improve performance. Our results indicate that ensemble models, particularly stacking and AdaBoost, demonstrate the highest performance, providing a valuable tool for early detection and risk assessment of heart disease. These findings highlight the potential of ML algorithms in healthcare applications, contributing to more accurate and efficient risk prediction.},
        keywords = {Machine Learning (ML), Cardiovascular Diseases Prediction (CVD), Random Forest Classifier (RFC), Logistic Regression (LR), k-Nearest Neighbors (KNN), decision trees, ensemble models, bagging, Ad- aBoost, gradient boosting, stacking, accuracy, precision, recall and F1-score},
        month = {January},
        }