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.

Social Determinants and Adverse Childhood Experiences as Predictors of Cannabis Use: A Machine Learning Analysis of 2023–2024 BRFSS Data

Social Determinants and Adverse Childhood Experiences as Predictors of Cannabis Use: A Machine Learning Analysis of 2023–2024 BRFSS Data

Organization: Research Collaboration
Role: Machine Learning Researcher and Data Engineer
Project Period: January-March 2026

Overview
Contributed to a machine learning research project investigating how adverse childhood experiences (ACEs) and social determinants of mental health (SDMH) predict cannabis use behaviors among U.S. adults. Using data from the 2023–2024 Behavioral Risk Factor Surveillance System (BRFSS), the study examined factors associated with both cannabis use occurrence and frequency through predictive modeling and feature importance analysis.

Key Contributions

  • Engineered and cleaned large-scale BRFSS datasets for machine learning analysis.
  • Developed and evaluated machine learning models to predict cannabis use outcomes.
  • Performed feature engineering, model validation, and performance assessment.
  • Conducted statistical analyses and interpretation of model outputs.
  • Wrote the Methods and Results sections of the manuscript.
  • Contributed to data visualization and presentation of findings.

Publication Status

  • Manuscript submitted: May 26, 2026

Citation

Han, E., Shin, J., Hyun, J., & Park, Y. (2026). Social Determinants and Adverse Childhood Experiences as Predictors of Cannabis Use: A Machine Learning Analysis of 2023–2024 BRFSS Data. Manuscript submitted for publication.