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

被引:3
|
作者
Alexos, Antonios [1 ]
Kokkotis, Christos [2 ]
Moustakidis, Serafeim [3 ]
Papageorgiou, Elpiniki [4 ]
机构
[1] Univ Calif Irvine, Dept Comp Sci, Irvine, CA 92697 USA
[2] Ctr Res & Technol Hellas, Inst Bioecon & Agritechnol, Volos, Greece
[3] AIDEAS OU, Narva Mnt 5, Tallinn, Harju Maakond, Estonia
[4] Univ Thessaly, Fac Technol, Energy Syst Dept, Geopolis Campus, Larisa 41500, Greece
基金
欧盟地平线“2020”;
关键词
machine learning; knee osteoarthritis; pain prediction; feature selection; physical function; knee joint; IDENTIFICATION;
D O I
10.1109/iisa50023.2020.9284379
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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.
引用
收藏
页码:240 / 246
页数:7
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