• English
    • Ελληνικά
    • Deutsch
    • français
    • italiano
    • español
  • español 
    • English
    • Ελληνικά
    • Deutsch
    • français
    • italiano
    • español
  • Login
Ver ítem 
  •   DSpace Principal
  • Επιστημονικές Δημοσιεύσεις Μελών ΠΘ (ΕΔΠΘ)
  • Δημοσιεύσεις σε περιοδικά, συνέδρια, κεφάλαια βιβλίων κλπ.
  • Ver ítem
  •   DSpace Principal
  • Επιστημονικές Δημοσιεύσεις Μελών ΠΘ (ΕΔΠΘ)
  • Δημοσιεύσεις σε περιοδικά, συνέδρια, κεφάλαια βιβλίων κλπ.
  • Ver ítem
JavaScript is disabled for your browser. Some features of this site may not work without it.
Todo DSpace
  • Comunidades & Colecciones
  • Por fecha de publicación
  • Autores
  • Títulos
  • Materias

Prediction of pain in knee osteoarthritis patients using machine learning: Data from Osteoarthritis Initiative

Thumbnail
Autor
Alexos A., Kokkotis C., Moustakidis S., Papageorgiou E., Tsaopoulos D.
Fecha
2020
Language
en
DOI
10.1109/IISA50023.2020.9284379
Materia
Health
Machine learning
Predictive analytics
Elder peoples
Feature subset
Knee osteoarthritis
Risk factors
Voting systems
Learning algorithms
Institute of Electrical and Electronics Engineers Inc.
Mostrar el registro completo del ítem
Resumen
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.
URI
http://hdl.handle.net/11615/70436
Colecciones
  • Δημοσιεύσεις σε περιοδικά, συνέδρια, κεφάλαια βιβλίων κλπ. [19735]
htmlmap 

 

Listar

Todo DSpaceComunidades & ColeccionesPor fecha de publicaciónAutoresTítulosMateriasEsta colecciónPor fecha de publicaciónAutoresTítulosMaterias

Mi cuenta

AccederRegistro
Help Contact
DepositionAboutHelpContacto
Choose LanguageTodo DSpace
EnglishΕλληνικά
htmlmap