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 Roy, Avijeet Emmanuel; Shoran, Preety; Vidushi Title A name given to the resource Data Analytics in Diabetes Treatment: Approaches and Applications Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived AIP Conference Proceedings;Volume;3386;Issue;1;Article No.;20100; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1063/5.0323697" target="_blank" rel="noreferrer noopener">https://doi.org/10.1063/5.0323697</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105035995959?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105035995959?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 Roy A.E., CHRIST (Deemed To Be University, Delhi, India; Shoran P., CHRIST (Deemed To Be University, Delhi, India; Vidushi, CHRIST (Deemed To Be University, Delhi, India Description An account of the resource These days, managing diabetes has become particularly more beneficial with advancements in data analytics, using machine learning, predictive analytics, and patient-generated health data to optimize the outcome for patients. This paper explores the latest techniques and innovations in this field, including predictive modelling, wearable technology integration, and artificial intelligence for better personalized care. The study covers various analytical frameworks, compares the performance of multiple machine learning models, and discusses future directions in the integration of data analytics with telemedicine for diabetes care. The following words refer to diabetes management, data analytics, predictive simulation, AI, and algorithms for learning, wearable technologies, patient-generated health data, predictive analytics, continuous glucose monitoring, health informatics, personalized care, health data privacy, predictive algorithms, electronic health records, diabetes complications, telemedicine, feature selection, and model evaluation along with having patient-centric systems and chronic disease management. 2026 American Institute of Physics Inc.. All rights reserved. Subject The topic of the resource Brain Tumor Detection; Comparative Analysis; DenseNet-121; MobileNetV2 Publisher An entity responsible for making the resource available American Institute of Physics Relation A related resource ISSN: 0094243X; 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