Predicting temporomandibular disorders in adults using interpretable machine learning methods: a model development and validation study.

ABSTRACT:

Introduction

Temporomandibular disorders (TMD) have a high prevalence and complex etiology. The purpose of this study was to apply a machine learning (ML) approach to identify risk factors for the occurrence of TMD in adults and to develop and validate an interpretable predictive model for the risk of TMD in adults.

Methods

A total of 949 adults who underwent oral examinations were enrolled in our study. 5 different ML algorithms were used for model development and comparison, and feature selection was performed by feature importance ranking and feature decreasing methods. Several evaluation indexes, including the area under the receiver-operating-characteristic curve (AUC), were used to compare the predictive performance. The precision-recall curve (PR), calibration curve,

SUBMITTER: Cui Y 

PROVIDER: S-EPMC11573567 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature