A machine learning approach to predict postoperative sleep disturbance after total knee arthroplasty: a comparative study of multiple algorithms.

ABSTRACT:

Background

Postoperative sleep disturbance (PSD) is a common complication following total knee arthroplasty (TKA), which negatively impacts patient recovery. Despite the critical need for early detection and management, there is limited research on predictive models for early PSD, particularly those integrating machine learning (ML) techniques.

Objective

This study aimed to develop a predictive model for early PSD following TKA using ML algorithms, identify key predictive factors, and provide an interpretable model to guide clinical decision-making.

Methods

The study included 505 patients who underwent TKA. Clinical data were collected at three stages: preoperatively, intraoperatively, and postoperatively. Ten MLa models, including logistic regression, support vector

SUBMITTER: Zhang YX 

PROVIDER: S-EPMC12627052 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature