Jihoon Shin

Jihoon Shin

he/his

Project Specialist at Syracuse University with 7 years of experience in IT systems management and 2 years of experience in machine learning research.

Machine Learning Prediction of Depression Using ACEs and SDMH

Adverse Childhood Experiences and Social Determinants of Mental Health as Predictors of Adult Depression: A Machine Learning Approach

Organization: The University of Texas at El Paso (UTEP) / Journal of Counseling & Development
Role: Co-Author (Machine Learning Methodology & Data Analysis)
Project Period: September–December 2025
Publication: Han, E., & Shin, J. (2026). Adverse Childhood Experiences and Social Determinants of Mental Health as Predictors of Adult Depression: A Machine Learning Approach. Journal of Counseling & Development. https://doi.org/10.1002/jcad.70029

Overview
Co-authored a peer-reviewed research article applying machine learning methods to examine how adverse childhood experiences (ACEs) and social determinants of mental health (SDMH) predict adult depressive disorders using the 2023 Behavioral Risk Factor Surveillance System (BRFSS) dataset. The study demonstrates how interpretable machine learning models can support trauma-informed counseling practice and improve early identification of individuals at risk for depression.

Key Contributions

  • Designed and implemented machine learning models (Logistic Regression, Naïve Bayes, Decision Tree, Random Forest, and XGBoost) to predict adult depressive disorder using large-scale survey data.
  • Led data preprocessing and feature engineering from 39 predictors derived from ACEs and SDMH variables in the BRFSS dataset.
  • Applied class-imbalance handling techniques and performed model evaluation using ROC-AUC, PR-AUC, precision, recall, and F1-score.
  • Conducted interpretable ML analysis using SHAP (SHapley Additive exPlanations) to identify key predictors influencing depression risk across demographic groups.
  • Contributed to methodological interpretation and discussion linking machine learning insights with trauma-informed counseling practice.