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 Book Chapter Faculty Publications- Book Chapter 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 Hormese, Tessa; Rajesh, R. Title A name given to the resource Fake News Detection in Healthcare Using Machine Learning 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 Mathematics and Computer Science for Real-World Applications;pp.235-250 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1002/9781394275359.ch15" target="_blank" rel="noreferrer noopener">https://doi.org/10.1002/9781394275359.ch15</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105013712230?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105013712230?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 Hormese T., Department of Statistics and Data Science, Christ (Deemed to be University), Bangalore, India; Rajesh R., Department of Statistics and Data Science, Christ (Deemed to be University), Bangalore, India Description An account of the resource The internet has revolutionary power in todays society, acting as an unmatched catalyst for technical innovation, worldwide connectedness, and information dissemination. It has transformed communication and made knowledge more accessible to all, and given people, companies, and society the tools they need to prosper in the connected digital world. However, this power is responsible for navigating issues, such as the proliferation of fake news and safeguarding information integrity. As peoples health comes first, false information about it might have disastrous consequences. Even for the most knowledgeable professionals in the field, identifying false information about health can be difficult because of the variety of factors that must be considered. New advances in machine learning have enabled automatic classification of bogus news. For the detection of fake news correctly, we must train the automation in such a way that it captures the bogus correctly, and for that the data we input is of at most importance and, in fact, the most important as well. To enhance the models capacity to discern between authentic and fake news, this study investigates the extraction of structural and semantic information from text using a combination of named entity recognition and syntactic parsing. Utilizing these characteristics, we trained a variety of machine learning algorithms, assessed their effectiveness, and found that the Random Forest classifier outperformed the others in classification. 2025 Scrivener Publishing LLC. Subject The topic of the resource Fake news; Healthcare; Natural language processing; Vectorization Publisher An entity responsible for making the resource available wiley Relation A related resource ISBN: 978-139427535-9; 978-139427532-8; Language A language of the resource English Type The nature or genre of the resource Book chapter 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