A machine learning-based approach to decipher multi-etiology of knee osteoarthritis onset and deterioration

ABSTRACT:

Objectives

By deploying a novel combination of machine learning approaches, we aim to investigate the contributions of each local and systemic risk factors in multi-etiology of knee osteoarthritis (KOA) to disease onset and deterioration.

Methods

A machine-learning-based KOA progression prediction model is developed using the data from the National Institute of Health Osteoarthritis Biomarkers Consortium. According to Kellgren-Lawrence (KL) grade of plain radiographs at baseline, the subjects are divided into either KOA onset or deterioration study groups. The disease progression is defined as the changes in both joint space width (JSW) and WOMAC pain score. In addition to radiographic and symptomatic data, the anthropological particulars, history of the knee injury and s

SUBMITTER: Chan L 

PROVIDER: S-EPMC9718099 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature