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 Kumar, Manoj; Sharan, Mudita; Shoran, Preety Title A name given to the resource Explainable AI for Heart Disease prediction: A Clinical Transparency Route Experiment 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/ICDISS68238.2025.11320656" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICDISS68238.2025.11320656</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033597851?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033597851?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 Kumar M., Department of Computer Sciences, CHRIST University, Bangalore, India; Sharan M., Department of Computer Sciences, CHRIST University, Bangalore, India; Shoran P., Department of Computer Sciences, CHRIST University, Bangalore, India Description An account of the resource In this paper, a proposeable explainable machine learning procedure on estimating the danger of heart attack will be proposed with a stacked ensemble of XGBoost, Random Forest, and Multi-layered perceptron (MLP). The data set of UCI Heart Disease was preprocessed by normalization, imputation, and SMOTE to address the imbalance problem and the variables were optimized with the help of the feature engineering. The model performance was measured using accuracy, precision, recall, F1-score and ROC-AUC. In order to make the results more interpretable, Explainable AI were applied with SHAP and LIME, and the most relevant risk factors including troponin, cholesterol, and blood pressure were indicated.. In this paper, it is shown that ensemble learning in XAI can yield plausible, interpretable, and clinically practical data to complement enhanced cardiovascular diagnostics. 2025 IEEE. Subject The topic of the resource Clinical Decision Support; Ensemble Learning; Ex explainable AI; Heart Attack Prediction; Random Forest; SHAP; XGBoost 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