dc.creator | Alexos A., Kokkotis C., Moustakidis S., Papageorgiou E., Tsaopoulos D. | en |
dc.date.accessioned | 2023-01-31T07:30:56Z | |
dc.date.available | 2023-01-31T07:30:56Z | |
dc.date.issued | 2020 | |
dc.identifier | 10.1109/IISA50023.2020.9284379 | |
dc.identifier.isbn | 9781665422284 | |
dc.identifier.uri | http://hdl.handle.net/11615/70436 | |
dc.description.abstract | Knee 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.iso | en | en |
dc.source | 11th International Conference on Information, Intelligence, Systems and Applications, IISA 2020 | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099210015&doi=10.1109%2fIISA50023.2020.9284379&partnerID=40&md5=3ad03355080d698a9ee8669270a32b7d | |
dc.subject | Health | en |
dc.subject | Machine learning | en |
dc.subject | Predictive analytics | en |
dc.subject | Elder peoples | en |
dc.subject | Feature subset | en |
dc.subject | Knee osteoarthritis | en |
dc.subject | Risk factors | en |
dc.subject | Voting systems | en |
dc.subject | Learning algorithms | en |
dc.subject | Institute of Electrical and Electronics Engineers Inc. | en |
dc.title | Prediction of pain in knee osteoarthritis patients using machine learning: Data from Osteoarthritis Initiative | en |
dc.type | conferenceItem | en |