Can adverse childhood experiences predict chronic health conditions? Development of trauma-informed, explainable machine learning models.

ABSTRACT:

Introduction

Decades of research have established the association between adverse childhood experiences (ACEs) and adult onset of chronic diseases, influenced by health behaviors and social determinants of health (SDoH). Machine Learning (ML) is a powerful tool for computing these complex associations and accurately predicting chronic health conditions.

Methods

Using the 2021 Behavioral Risk Factor Surveillance Survey, we developed several ML models-random forest, logistic regression, support vector machine, Naïve Bayes, and K-Nearest Neighbor-over data from a sample of 52,268 respondents. We predicted 13 chronic health conditions based on ACE history, health behaviors, SDoH, and demographics. We further assessed each variable's importance in outcome prediction for model int

SUBMITTER: Afzal HB 

PROVIDER: S-EPMC10851779 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature