Data-driven prediction model for periodontal disease based on correlational feature analysis and clinical validation.

ABSTRACT:

Objectives

This study aimed to investigate the performance and reliability of data-driven models employing correlational feature analysis and clinical validation for predicting periodontal disease.

Methods

The 7th Korea National Health and Nutrition Examination Survey (n = 10,654) was used for correlation analysis to identify significant risk factors for periodontitis. Periodontal prediction models were developed with the selected factors and database, followed by internal validation with 5-fold cross-validation and 1000 bootstrap resampling. External validation was conducted with clinical data (n = 120) collected through self-reported questionnaires, clinical periodontal parameters, and radiographic image analysis. Predictive performance was assessed for logis

SUBMITTER: Beak W 

PROVIDER: S-EPMC11193031 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature