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 Joseph, Akash Varughese; Mariya, Isa; Thontadari, C. Title A name given to the resource A Deep Learning Approach to Clinical Decision Support in Heart Disease Diagnosis 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 Lecture Notes in Networks and Systems;Volume;1354 LNNS;pp.351-361 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-4880-1_28" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-4880-1_28</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105019309862?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105019309862?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 Joseph A.V., Department of Computer Science, Christ University, Bangalore, India; Mariya I., Department of Computer Science, Christ University, Bangalore, India; Thontadari C., Department of Computer Science, Christ University, Bangalore, India Description An account of the resource Heart disease is the dominant cause of extinction worldwide, emphasizing the importance of early diagnosis and treatment planning. In this article, the authors developed a Clinical Decision Support System (CDSS) for heart disease prediction using deep learning techniques. This system will suggest a neural network architecture with Leaky ReLU as the activation function in the hidden layers and Sigmoid as the activation function in the output layer for binary classification. The configuration neural network is enhanced across three to nine hidden layers. The proposed approach is evaluated using accuracy as the measurable value on five multivariate datasets. By integrating advanced deep learning with clinical expertise, this study aims to enhance predictive accuracy, contributing to reduced heart disease mortality. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Activation functions; Decision support system; Deep learning; Heart disease prediction; LeakyReLu; Medical diagnosis Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981964879-5; 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