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 Article Faculty Publications -Articles 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, Amaravarapu Pramod; Macha, Yuvaraj; Kumar, A. Siva; Sekhar, Buna; Krishnapriya, Singamaneni; Chanti, S. Title A name given to the resource HEART FAILURE DETECTION USING OPTIMIZATION ALGORITHMS 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 Journal of Theoretical and Applied Information Technology;Volume;103;Issue;7;pp.2594-2611 Identifier An unambiguous reference to the resource within a given context <a href="https://www.scopus.com/pages/publications/105002743438?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105002743438?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 A.P., CSE-(Cys,DS) and AI &amp; DS, VNR Vignana Jyothi Institute of ENgineering and Technology, Bachupally, Hyderbad, India; Macha Y., Department of Mathematics, Matrusri Engineering College, Telengana, Hyderbad, India; Kumar A.S., Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation (KLEF), KL University, Vijayawada Campus, Green Fields, Andhra Pradesh, Vaddeswaram, India; Sekhar B., CSE, CMR Institute of Technology, Kandlakoya,Medchal, Telengana, Hyderbad, India; Krishnapriya S., CSE-(Cys,DS) and AI &amp; DS, VNR Vignana Jyothi Institute of ENgineering and Technology, Telenagan, Hyderbad, India; Chanti S., Department of Computer Science, Christ University, Karnataka, Bangaluru, India Description An account of the resource Heart failure (HF) remains a significant global health challenge, requiring early and precise detection to improve clinical outcomes and reduce mortality rates. Traditional diagnostic approaches often fail to capture the complexity of HF pathophysiology, necessitating advanced computational methods for accurate prediction. In this study, we propose a novel optimized Stacked Support Vector Machine (S-SVM) framework, integrating multiple SVM classifiers with diverse kernel functions to enhance predictive accuracy. A genetic algorithm (GA) is employed to fine-tune hyperparameters, ensuring model robustness and generalizability across patient populations. The model is rigorously evaluated on the UCI Heart Failure Clinical Records Dataset and the Framingham Heart Study Dataset, demonstrating superior performance in accuracy (95.7%), precision (0.90), recall (0.87), and AUC (0.96) compared to conventional machine learning techniques. The proposed system effectively balances computational efficiency with clinical interpretability, making it a promising tool for early-stage HF detection and risk stratification. This research advances the intersection of machine learning and cardiovascular diagnostics, offering a scalable and adaptive solution for real-world healthcare applications. Little Lion Scientific. Subject The topic of the resource Cardiovascular Diagnostics; Clinical DeciSion Support; Genetic Algorithm; Heart Failure Prediction; Machine Learning; Stacked SVM Publisher An entity responsible for making the resource available Little Lion Scientific Relation A related resource ISSN: 19928645; Language A language of the resource English Type The nature or genre of the resource Article 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