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 George, Jossy; Yadav, Jai; Nair, Akhil M. Title A name given to the resource Benchmarking Ensemble Methods: Stacking, Hard Voting, and Soft Voting 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;1371 LNNS;pp.315-328 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-5723-0_23" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-5723-0_23</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105018920550?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105018920550?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 George J., CHRIST (Deemed to be University), Bangalore, India; Yadav J., CHRIST (Deemed to be University), Bangalore, India; Nair A.M., CHRIST (Deemed to be University), Bangalore, India Description An account of the resource This study evaluates three ensemble techniquesbasic stacking, hard voting, and soft votingfor predicting diabetes onset using the Pima Indians Diabetes dataset. While traditional methods often focus on single models, this research emphasizes the benefits of combining models like Cat Boost, random forest, logistic regression, linear discriminant analysis, and gradient boosting classifier (LightGBM) within ensemble frameworks. The models were rigorously assessed using metrics for evaluation such as AUC-ROC curves, confusion matrices, F1 scores, etc. The advanced calibrated model achieved the highest performance, with an accuracy of 90.10%, precision of 90.32%, recall of 81.16%, and an F1 score of 85.50%. The soft voting model also delivered strong results, with an accuracy of 89.06%, precision of 87.50%, recall of 81.16%, and F1 score of 84.21%. In comparison, the hard voting model recorded an accuracy of 88.02%, precision of 85.94%, recall of 79.71%, and F1 score of 82.71%. These results highlight the potential of advanced ensemble methods to enhance predictive accuracy. Future work could involve integrating these models with real-time monitoring systems for improved healthcare diagnostics and applying them to diverse datasets and medical conditions. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource AUC-ROC curve; Confusion matrix; F1 score; Homogenous stacking; Plant disease prediction Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981965722-3; 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