Development of a predictive model for 1-year postoperative recovery in patients with lumbar disk herniation based on deep learning and machine learning.

ABSTRACT:

Background

The aim of this study is to develop a predictive model utilizing deep learning and machine learning techniques that will inform clinical decision-making by predicting the 1-year postoperative recovery of patients with lumbar disk herniation.

Methods

The clinical data of 470 inpatients who underwent tubular microdiscectomy (TMD) between January 2018 and January 2021 were retrospectively analyzed as variables. The dataset was randomly divided into a training set (n = 329) and a test set (n = 141) using a 10-fold cross-validation technique. Various deep learning and machine learning algorithms including Random Forests, Extreme Gradient Boosting, Support Vector Machines, Extra Trees, K-Nearest Neighbors, Logistic Regression, Light Gradient Boosting Machi

SUBMITTER: Chen Y 

PROVIDER: S-EPMC11197993 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature