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 Gupta, Ritwika Das; Chatterjee, Soham; Latifi, Shpetim; Behera, Tapan Kumar Title A name given to the resource Unveiling Patterns, Visualizations, and Trends from Patient Diabetes Data 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 Electrical Engineering;Volume;1269;pp.321-332 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-97-9515-4_24" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-9515-4_24</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105003895165?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105003895165?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 Gupta R.D., Department of Statistics and Data Science, CHRIST Deemed to be University, Bangalore, India; Chatterjee S., Department of Statistics and Data Science, CHRIST Deemed to be University, Bangalore, India; Latifi S., Department of Computer Science, AAB College, Pristina, Kosovo; Behera T.K., Technical Architect, Independent Researcher, Boston, MA, United States Description An account of the resource The important role that exploratory data analysis, or EDA, plays in the context of diabetes prediction is explored in this work. EDA is used as a key component of a multimodal strategy to identify unique characteristics linked to diabetes. EDA offers insights that aid in the creation of prediction models by sifting through the complex patterns present in the medical data. The focus is on using EDA to fully grasp the data landscape while also comprehending the distinct features of diabetes. This investigation is critical to accurately categorizing people into discrete risk groups and emphasizes the use of domain-specific knowledge in enhancing diabetes prediction techniques. The research suggests using specific EDA techniques to gain deep insights and lead proactive responses. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Classification algorithms; Cross-validation; Downsampling; Exploratory data analysis; Feature engineering; Healthcare decision-making; Proactive interventions Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 18761100; ISBN: 978-981979514-7; 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