Predicting self-perceived general health status using machine learning: an external exposome study.

ABSTRACT:

Background

Self-perceived general health (SPGH) is a general health indicator commonly used in epidemiological research and is associated with a wide range of exposures from different domains. However, most studies on SPGH only investigated a limited set of exposures and did not take the entire external exposome into account. We aimed to develop predictive models for SPGH based on exposome datasets using machine learning techniques and identify the most important predictors of poor SPGH status.

Methods

Random forest (RF) was used on two datasets based on personal characteristics from the 2012 and 2016 editions of the Dutch national health survey, enriched with environmental and neighborhood characteristics. Model performance was determined using the area under the curve (AUC

SUBMITTER: Hoekstra J 

PROVIDER: S-EPMC10230687 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature