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

Jurriaan Hoekstra¹, Esther S Lenssen², Albert Wong³

¹National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands. jurriaan.hoekstra@rivm.nl.

BMC Public Health

|May 31, 2023

Summary

This summary is machine-generated.

Self-perceived general health is predicted by factors like life control, physical activity, loneliness, and finances. Improving these areas may enhance overall health perception.

Keywords:

Exposome Machine learning Random forest Self-perceived general health

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Area of Science:

Epidemiology
Environmental Health
Machine Learning

Background:

Self-perceived general health (SPGH) is a key health indicator but often studied with limited exposures.
The external exposome, encompassing environmental and lifestyle factors, is crucial for a comprehensive understanding of SPGH.
Previous research has not fully integrated the external exposome into SPGH predictive models.

Purpose of the Study:

To develop machine learning models predicting SPGH using comprehensive exposome data.
To identify key predictors of poor SPGH status within the external exposome.
To enhance understanding of factors influencing self-rated health.

Main Methods:

Random Forest (RF) machine learning models were applied to large-scale Dutch health survey data (2012 and 2016).
Datasets included personal, environmental, and neighborhood characteristics.
Model performance was evaluated using Area Under the Curve (AUC), and predictor importance was assessed via variable importance and partial dependence plots.

Main Results:

RF models demonstrated strong predictive performance for SPGH (AUCs of 0.864 and 0.890).
Key predictors identified were "Control of own life", "Physical activity", "Loneliness", and "Making ends meet".
Higher physical activity was associated with better SPGH, while lack of life control, loneliness, and financial difficulties were linked to poorer SPGH.

Conclusions:

Mental wellbeing, physical activity, social connection (loneliness), and financial stability are significant predictors of SPGH.
Environmental and neighborhood factors showed limited contribution to overall SPGH prediction in this model.
The study highlights the importance of psychological and socioeconomic factors in self-perceived health.