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 Balancing cerebrovascular disease data with integrated ensemble learning and SVM-SMOTE Subject The topic of the resource Bagging; Boosting; Cerebrovascular disease; Ensemble learning; Imbalanced classification; Machine learning; Stacking Description An account of the resource The paper addresses the challenge of imbalanced classification in the context of cerebrovascular diseases, including stroke, transient ischemic attack (TIA), and vascular dementia. The imbalanced nature of cerebrovascular disease datasets poses significant challenges to conventional machine learning algorithms, making precise diagnosis and effective management difficult. The aim of the paper is to propose a novel approach, the INTEL_SS algorithm, which combines ensemble learning techniques with Support Vector Machine-Synthetic Minority Over-sampling Technique (SVM-SMOTE) to effectively handle the imbalanced nature of cerebrovascular disease datasets. The goal is to improve the accuracy of diagnosis and management of cerebrovascular diseases through advanced machine learning techniques. The proposed methodology involves several key steps, including preprocessing, SVM-SMOTE, and ensemble learning. Preprocessing techniques are used to improve the quality of the dataset, SVM-SMOTE is employed to address class imbalance, and ensemble learning methods such as bagging, boosting, and stacking are utilized to improve overall classification performance. The experimental results demonstrate that the INTEL_SS algorithm outperforms existing methods in terms of accuracy, precision, recall, F1-score, and AUC-ROC. Performance metrics are used to assess the effectiveness of the proposed approach, and the results consistently show the superiority of INTEL_SS compared to state-of-the-art imbalanced classification algorithms. The paper concludes that the INTEL_SS algorithm has the potential to enhance the diagnosis and management of cerebrovascular diseases, offering new opportunities to apply machine learning techniques to improve healthcare outcomes. The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024. Creator An entity primarily responsible for making the resource Nithya R.; Kokilavani T.; Beena T.L.A. Source A related resource from which the described resource is derived Network Modeling Analysis in Health Informatics and Bioinformatics, Vol-13, No. 1 Publisher An entity responsible for making the resource available Springer Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s13721-024-00447-4" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s13721-024-00447-4</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188563501&amp;doi=10.1007%2Fs13721-024-00447-4&amp;partnerID=40&amp;md5=ccca9d152867bfd36dbe58be8a98630f" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188563501&amp;doi=10.1007%2fs13721-024-00447-4&amp;partnerID=40&amp;md5=ccca9d152867bfd36dbe58be8a98630f</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 21926662 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 Nithya R., Department of Computer Science, St. Josephs College (Autonomous), Affiliated to Bharathidasan University, Trichy, India; Kokilavani T., Department of Computer Science, Christ University, Karnataka, Bangalore, India; Beena T.L.A., Department of Computer Science, St. Josephs College (Autonomous), Affiliated to Bharathidasan University, Trichy, India