Measuring the Severity of the Signs of Eating Disorders Using Machine Learning Techniques

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dc.contributor.authorPrasanna, S.
dc.contributor.authorGulati, A.
dc.contributor.authorKarmakar, S.
dc.contributor.authorHiranmayi, M.Y.
dc.contributor.authorAnand Kumar, M.
dc.date.accessioned2026-02-06T06:33:59Z
dc.date.issued2024
dc.description.abstractThe paper presents the results submitted by Team SCaLAR-NITK for task 3 of eRisk Lab at CLEF 2024 [1]. The dataset provided by the task organizers consisted of 74 subjects for training and 18 for testing. We begin by describing the data cleaning and preprocessing steps. Subsequently, we outline various approaches used to address the problem, such as Word2Vec, TF-IDF, Backtranslation and Dimensionality Reduction, among others. Finally, we summarize the results obtained from each approach. Our solutions demonstrated strong performance, achieving the best results in 7 out of the 8 evaluated metrics. © 2024 Copyright for this paper by its authors.
dc.identifier.citationCEUR Workshop Proceedings, 2024, Vol.3740, , p. 881-887
dc.identifier.issn16130073
dc.identifier.urihttps://doi.org/
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/28984
dc.publisherCEUR-WS
dc.subjectBacktranslation
dc.subjectMachine Learning
dc.subjectTF-IDF
dc.subjectWord2Vec
dc.titleMeasuring the Severity of the Signs of Eating Disorders Using Machine Learning Techniques

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