Identifying significant structural factors associated with knee pain severity in patients with osteoarthritis using machine learning.

ABSTRACT: Our main objective was to use machine learning methods to identify significant structural factors associated with pain severity in knee osteoarthritis patients. Additionally, we assessed the potential of various classes of imaging data using machine learning techniques to gauge knee pain severity. The data of semi-quantitative assessments of knee radiographs, semi-quantitative assessments of knee magnetic resonance imaging (MRI), and MRI images from 567 individuals in the Osteoarthritis Initiative (OAI) were utilized to train a series of machine learning models. Models were constructed using five machine learning methods: random forests (RF), support vector machines (SVM), logistic regression (LR), decision tree (DT), and Bayesian (Bayes). Employing tenfold cross-validation, we selected th

SUBMITTER: Zhao Z 

PROVIDER: S-EPMC11208546 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature