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 Articles 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/. Title A name given to the resource AttGRU-HMSI: enhancing heart disease diagnosis using hybrid deep learning approach Subject The topic of the resource Attention-based gated recurrent unit network; Improved K-means clustering; Recursive feature elimination; Synthetic minority oversampling technique Description An account of the resource Heart disease is a major global cause of mortality and a major public health problem for a large number of individuals. A major issue raised by regular clinical data analysis is the recognition of cardiovascular illnesses, including heart attacks and coronary artery disease, even though early identification of heart disease can save many lives. Accurate forecasting and decision assistance may be achieved in an effective manner with machine learning (ML). Big Data, or the vast amounts of data generated by the health sector, may assist models used to make diagnostic choices by revealing hidden information or intricate patterns. This paper uses a hybrid deep learning algorithm to describe a large data analysis and visualization approach for heart disease detection. The proposed approach is intended for use with big data systems, such as Apache Hadoop. An extensive medical data collection is first subjected to an improved k-means clustering (IKC) method to remove outliers, and the remaining class distribution is then balanced using the synthetic minority over-sampling technique (SMOTE). The next step is to forecast the disease using a bio-inspired hybrid mutation-based swarm intelligence (HMSI) with an attention-based gated recurrent unit network (AttGRU) model after recursive feature elimination (RFE) has determined which features are most important. In our implementation, we compare four machine learning algorithms: SAE + ANN (sparse autoencoder + artificial neural network), LR (logistic regression), KNN (K-nearest neighbour), and nae Bayes. The experiment results indicate that a 95.42% accuracy rate for the hybrid model's suggested heart disease prediction is attained, which effectively outperforms and overcomes the prescribed research gap in mentioned related work. The Author(s) 2024. Creator An entity primarily responsible for making the resource Rao G.M.; Ramesh D.; Sharma V.; Sinha A.; Hassan M.M.; Gandomi A.H. Source A related resource from which the described resource is derived Scientific Reports, Vol-14, No. 1 Publisher An entity responsible for making the resource available Nature Research Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1038/s41598-024-56931-4" target="_blank" rel="noreferrer noopener">https://doi.org/10.1038/s41598-024-56931-4</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189302362&amp;doi=10.1038%2Fs41598-024-56931-4&amp;partnerID=40&amp;md5=2ed2b3b9b1b72920fc2132a33589f632" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189302362&amp;doi=10.1038%2fs41598-024-56931-4&amp;partnerID=40&amp;md5=2ed2b3b9b1b72920fc2132a33589f632</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 20452322; PubMed ID: 38570560 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Rao G.M., Department of Computer Science and Engineering, Indian Institute of Technology (Indian School of Mines), Jharkhand, Dhanbad, 826004, India, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad, 500075, India; Ramesh D., Department of Computer Science and Engineering, Indian Institute of Technology (Indian School of Mines), Jharkhand, Dhanbad, 826004, India, Department of Computer Science, University of Economics and Human Sciences, Warsaw, Poland; Sharma V., Computer Science Department, Christ University, Delhi NCR Campus, Delhi NCR, Ghaziabad, India; Sinha A., Department of Computer Science, ICFAI Tech School, ICFAI University, Jharkhand, Ranchi, India; Hassan M.M., Computer Science and Engineering, Discipline Khulna University, Khulna, 9208, Bangladesh; Gandomi A.H., Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, 2007, NSW, Australia, University Research and Innovation Center (EKIK), uda University, Budapest, 1034, Hungary