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 An hybrid technique for optimized clustering of EHR using binary particle swarm and constrained optimization for better performance in prediction of cardiovascular diseases Subject The topic of the resource Binary particle swarm optimization (BPS); CVD; EHRs; LSTM and Centroid based clustering; Machine learning Description An account of the resource The significant adoption of Electronic Health Records (EHR) in healthcare has furnished large new quantities of information for statistical machine gaining knowledge of researchers in their efforts to version and expects affected person health popularity, doubtlessly permitting novel advances in treatment. Unsupervised system learning is the project of studying styles in facts where no labels are present. In comparison to loads of optimization problems, an most beneficial clustering end result does not exist. One-of-a-kind algorithms with special parameters produce special clusters, and none can be proved to be the quality answer given that numerous good walls of the records might be found. In the previous work, a novel Two-fold clustering technique which uses the Long Short Term Memory (LSTM) technique (TFC: LSTM) for the prediction of Cardiovascular Disease (CVD) was proposed. The proposed model was fond to be experimentally efficient; however when applied to large EHR data, the model suffered from optimization issues on the number of clusters formed and time complexity. In order to overcome the drawbacks, this paper proposes a hybrid method of optimization using the Binary Particle Swarm (BPS) and Constrained Optimization (CO) for optimizing the number of clusters produced and to increase the efficiency in terms of decreasing the time complexity. 2022 The Authors Creator An entity primarily responsible for making the resource R D.M.; Kuwelkar S.; Sivakumar R. Source A related resource from which the described resource is derived Measurement: Sensors, Vol-25 Publisher An entity responsible for making the resource available Elsevier Ltd Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.measen.2022.100577" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.measen.2022.100577</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144306334&amp;doi=10.1016%2Fj.measen.2022.100577&amp;partnerID=40&amp;md5=dadf140f536f8db11a7d06274c2cd863" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144306334&amp;doi=10.1016%2fj.measen.2022.100577&amp;partnerID=40&amp;md5=dadf140f536f8db11a7d06274c2cd863</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 26659174 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 R D.M., Lean Operations and Systems, School of Business and Management, CHRIST (Deemed to be University), Bangalore, India; Kuwelkar S., Electronics and Telecommunications Department, Goa College of Engineering, Goa, Farmagudi, India; Sivakumar R., Department of Computer Science, CHRIST(Deemed to be University), Bangalore, India