Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Baruah, Anamika; Poonia, Ramesh Chandra; Shanbhog, Manjula Title A name given to the resource A Hybrid Stacked Ensemble Model for Heart Disease Prediction Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings of International Conference on Digital Innovations for Sustainable Solutions, ICDISS 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICDISS68238.2025.11320645" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICDISS68238.2025.11320645</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033633612?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033633612?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Baruah A., Department of Computer Science, CHRIST University, Bangalore, India; Poonia R.C., Department of Computer Science, CHRIST University, Bangalore, India; Shanbhog M., Department of Computer Science, CHRIST University, Bangalore, India Description An account of the resource Cardiovascular Diseases (CVDs), especially heart attacks, are resulting in high rates of death worldwide, which highlights the need for early prediction systems. This paper deals with advanced ML and DL methods to predict heart attacks with a pre-processed clinical dataset. 6 models were used: a Hybrid Stacked Model combined with Logistic Regression, Random Forest, and XGBoost using a neural meta-learner; CNN with LSTM, BiGRU, and dense layers; an RNN with BiLSTM; and an XGBoost method using deep feature representations. Data preprocessing involved feature scaling and class balancing with the help of SMOTE. Model performance is being measured by Accuracy, Precision, Recall, and F1-Score. Hybrid Stacked Model had the highest accuracy (94.24%) and F1-score (94.12%), while CNN + LSTM had the best recall (95.96%), to reduce false negatives. XGBoost with deep features demonstrated competitive accuracy (91.22%) and transparency. These results point to the efficiency of hybrid and sequential deep learning models in cardiovascular risk prediction. In the future, research will be focused on real-time patient data integration, federated learning for privacy, and personalized health promotion using IoT-based monitoring. 2025 IEEE. Subject The topic of the resource BiGRU; Cardiovascular Diseases; CNN; Heart Attack Predication; LSTM Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155641-9; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online