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; Ramadani, Lauresha Title A name given to the resource Optimizing Healthcare: Enhancing Disease Management with Recommendation Systems 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.119-127 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-97-9515-4_9" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-9515-4_9</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105003895007?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105003895007?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; Ramadani L., Department of Computer Science, AAB College, Pristina, Kosovo Description An account of the resource This paper explores a data-driven disease recommendation system for medical professionals based on symptoms. The technology examines symptom patterns to recommend diseases from large datasets by utilizing collaborative filtering and data analytics. To provide individualized disease recommendations based on symptom severity, it goes through data preprocessing and uses techniques like collaborative filtering and cosine similarity. Even if the technology is promising, disease predictions might be strengthened. It seeks to support early disease prediction and offer patients and healthcare professionals individualized guidance. This system demonstrates the potential of technology in healthcare decision-making using a basic Tkinter application. More improvements are anticipated as a result of data-driven approach advancements, which will improve patient care and optimize healthcare procedures. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Collaborative filtering; Cosine similarity healthcare optimization; Data analytics; Disease prediction; Disease recommendation system; Healthcare decision-making; Personalized healthcare; Symptom severity; Symptom-based diagnosis 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