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dc.creatorAlexos A., Kokkotis C., Moustakidis S., Papageorgiou E., Tsaopoulos D.en
dc.date.accessioned2023-01-31T07:30:56Z
dc.date.available2023-01-31T07:30:56Z
dc.date.issued2020
dc.identifier10.1109/IISA50023.2020.9284379
dc.identifier.isbn9781665422284
dc.identifier.urihttp://hdl.handle.net/11615/70436
dc.description.abstractKnee Osteoarthritis(KOA) is a serious disease that causes a variety of symptoms, such as severe pain and it is mostly observed in the elder people. The main goal of this study is to build a prognostic tool that will predict the progression of pain in KOA patients using data collected at baseline. In order to do that we leverage a feature importance voting system for identifying the most important risk factors and various machine learning algorithms to classify, whether a patient's pain with KOA, will stabilize, increase or decrease. These models have been implemented on different combinations of feature subsets, and results up to 84.3% have been achieved with only a small amount of features. The proposed methodology demonstrated unique potential in identifying pain progression at an early stage therefore improving future KOA prevention efforts. © 2020 IEEE.en
dc.language.isoenen
dc.source11th International Conference on Information, Intelligence, Systems and Applications, IISA 2020en
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85099210015&doi=10.1109%2fIISA50023.2020.9284379&partnerID=40&md5=3ad03355080d698a9ee8669270a32b7d
dc.subjectHealthen
dc.subjectMachine learningen
dc.subjectPredictive analyticsen
dc.subjectElder peoplesen
dc.subjectFeature subseten
dc.subjectKnee osteoarthritisen
dc.subjectRisk factorsen
dc.subjectVoting systemsen
dc.subjectLearning algorithmsen
dc.subjectInstitute of Electrical and Electronics Engineers Inc.en
dc.titlePrediction of pain in knee osteoarthritis patients using machine learning: Data from Osteoarthritis Initiativeen
dc.typeconferenceItemen


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