Development of a Machine Learning-Based Model to Predict Timed-Up-and-Go Test in Older Adults.

ABSTRACT:

Introduction

The measurement of physical frailty in elderly patients with orthopedic impairments remains a challenge due to its subjectivity, unreliability, time-consuming nature, and limited applicability to uninjured individuals. Our study aims to address this gap by developing objective, multifactorial machine models that do not rely on mobility data and subsequently validating their predictive capacity concerning the Timed-up-and-Go test (TUG test) in orthogeriatric patients.

Methods

We utilized 67 multifactorial non-mobility parameters in a pre-processing phase, employing six feature selection algorithms. Subsequently, these parameters were used to train four distinct machine learning algorithms, including a generalized linear model, a support vector machine, a random f

SUBMITTER: Kraus M 

PROVIDER: S-EPMC10606325 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature