Childhood Environmental Instabilities and Their Behavioral Implications: A Machine Learning Approach to Studying Adverse Childhood Experiences.

ABSTRACT: Adverse childhood experiences (ACEs) include a range of abusive, neglectful, and dysfunctional household behaviors that are strongly associated with long-term health problems, mental health conditions, and societal difficulties. The study aims to uncover significant factors influencing ACEs in children aged 0-17 years and to propose a predictive model that can be used to forecast the likelihood of ACEs in children. Machine learning models are applied to identify and analyze the relationships between several predictors and the occurrence of ACEs. Key performance metrics such as AUC, F1 score, recall, and precision are used to evaluate the predictive strength of different factors on ACEs. Family structures, especially non-traditional forms such as single parenting, and the frequency of reloc

SUBMITTER: Codjoe PM 

PROVIDER: S-EPMC11200692 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature