Comparison of model feature importance statistics to identify covariates that contribute most to model accuracy in prediction of insomnia.

ABSTRACT:

Importance

Sleep is critical to a person's physical and mental health and there is a need to create high performing machine learning models and critically understand how models rank covariates.

Objective

The study aimed to compare how different model metrics rank the importance of various covariates.

Design, setting, and participants

A cross-sectional cohort study was conducted retrospectively using the National Health and Nutrition Examination Survey (NHANES), which is publicly available.

Methods

This study employed univariate logistic models to filter out strong, independent covariates associated with sleep disorder outcome, which were then used in machine-learning models, of which, the most optimal was chosen. The machine-learning model was used to rank mode

SUBMITTER: Huang AA 

PROVIDER: S-EPMC11218970 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature