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 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/. Creator An entity primarily responsible for making the resource Rajagopal, Manikandan; Mandala, Gowthamm; Ranganathan, Nagendran; Varthan Velumani, Manoj; Patro, Pramoda; Arumugam, Shankar Title A name given to the resource Integrating Hesitant Fuzzy Sets with Machine Learning for Enhanced Healthcare Predictive Analytics 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 Journal of Fuzzy Extension and Applications;Volume;6;Issue;3;pp.484-505 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.22105/jfea.2025.490702.1715" target="_blank" rel="noreferrer noopener">https://doi.org/10.22105/jfea.2025.490702.1715</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105007700712?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105007700712?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 Rajagopal M., School of Business and Management, CHRIST (Deemed to be University), Bangalore, India; Mandala G., Biological Research Student, Purdue University, West Lafayette, United States; Ranganathan N., Department of Computer science and Engineering, Sri Ramakrishna Institute of Technology, Coimbatore, India; Varthan Velumani M., Axtria Inc, Berkeley Heights, 07922, NJ, United States; Patro P., School of Computer Science and Artificial Intelligence, SR University, Telangana, Warangal, India; Arumugam S., Department of ECE, Manakula Vinayagar Institute of Technology, Puducherry, India Description An account of the resource This study examines how Hesitant Fuzzy Sets (HFS) and Machine Learning (ML) might improve healthcare predictive analytics. HFS, which accommodates uncertainty and hesitation in decision-making, is used to improve healthcare projections. Predictive analytics methods struggle with data ambiguity and imprecision, resulting in poor decision-making. Traditional ML algorithms may not be able to collect hesitant information, resulting in less accurate patient outcomes and treatment recommendations. The Integrating Hesitant Fuzzy Sets with ML (IHFS-ML) framework overcomes these issues by integrating HFS flexibility with advanced ML approaches. This connection allows the representation of ambiguous patient data for better healthcare analytics. Data pre-processing in the IHFS-ML framework improves healthcare analytics prediction. These methods transform uncertain fuzzy data into an ML-friendly format. Disease prediction, patient risk assessment, and therapeutic effectiveness analysis are recommended. The approach aims to improve healthcare decision-making and deliver new insights by merging hesitant and ambiguous information. IHFS-ML uses HFS to characterize imprecise and confusing patient data. These HFS are combined with powerful ML classifiers like Random Forest (RF) and Logistic Regression. The IHFS-ML system outperforms current prediction accuracy and reliability methods, suggesting it might transform healthcare analytics. HFS improves ML model interpretability, improving patient outcomes and healthcare decisions. Compared to other methods, the IHFS-ML model improves prediction analysis reliability by 99.7%, scalability by 97.6%, data pre-processing efficiency by 97.1%, interpretability by 98.9%, and accuracy by 97.8%. 2025, Research Expansion Alliance (REA). All rights reserved. Subject The topic of the resource Clinical decision-making; Healthcare; Hesitant fuzzy sets; Machine learning; Patient data Publisher An entity responsible for making the resource available Research Expansion Alliance (REA) Relation A related resource ISSN: 27831442; Language A language of the resource English Type The nature or genre of the resource Article 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