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 Singh, Aaron; Alapatt, Bosco Paul; George, Jossy P Title A name given to the resource An Explainable AI-Driven Deep Learning Algorithm for Heart Disease Detection in Healthcare 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 6th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2025;pp.471-477 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICICV64824.2025.11085482" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICICV64824.2025.11085482</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105013073834?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105013073834?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 Singh A., Christ University, India; Alapatt B.P., Christ University, India; George J.P., Christ University, India Description An account of the resource The application of preprocessed Kaggle data serves as a subject of analysis to investigate heart attack prediction capabilities through machine learning models. The research examines performance outcomes of five algorithms which consist of K-Nearest Neighbors (KNN), Random Forest, Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost) and Convolutional Neural Networks (CNN). Random Forest together with XGBoost proved as the most accurate machine learning models when used for cardiovascular risk assessment. The researchers built a hybrid structure of CNN and SVM because it improved both data classification and feature extraction processes for better prediction outcomes. The training and evaluation process of models encountered difficulties because of overfitting along with high computational expenses and problems regarding optimal hyperparameter settings. The research stresses that explainable AI (XAI) methods should integrate into systems to enhance model interpretability and achieve trust from clinical professionals. Future initiatives seek real-time patient monitoring and innovative interpretability systems for heart attack prediction to enable person-specific diagnoses and optimal clinical choices in medical fields. 2025 IEEE. Subject The topic of the resource Cardiovascular Disease; CNN-SVM Hybrid; Healthcare AI; Heart Attack Prediction; Machine Learning; Medical Diagnostics; Predictive Analytics; Random Forest; XGBoost Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833151175-3; 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