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 Chatterjee, Soham; Gupta, Ritwika Das; Latifi, Shpetim; Behera, Tapan Kumar Title A name given to the resource Harnessing Insights for Optimizing Healthcare: Disease Prediction and Beyond 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.333-343 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-97-9515-4_25" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-9515-4_25</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105003894903?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105003894903?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 Chatterjee S., Department of Statistics and Data Science, CHRIST Deemed to be University, Bangalore, India; Gupta R.D., 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 This study offers a novel method for developing classification approaches for disease prediction. Exploratory analysis and meticulous data preprocessing were conducted to understand the relationships between symptoms and illnesses. The research involved assessing various machine learning models, including the random forest classifier, through cross-validation techniques, resulting in the identification of a high-performing model with an impressive accuracy rate. In addition, this study incorporates data visualization techniques to gain insights into symptomdisease connections. The studys focus on data visualization and optimization strategies in health demonstrates the potential to transform healthcare by providing precise diagnoses and predicting diseases, ultimately improving patient outcomes. This research underscores the efficacy of data-driven techniques and their integration into recommendation and disease prediction systems, emphasizing the significance of data visualization and optimization strategies in health. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Classification; Data analysis; Data visualization; Disease prediction; Healthcare; Machine learning; Optimization strategies 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