Purpose: This study aimed to identify latent classes based on major modifiable risk factors for coronary artery disease. Methods: This was a secondaryanalysis using data from the electronic medical records of 2,022 patients, who were newly diagnosed with coronary artery disease at auniversity medical center, from January 2010 to December 2015. Data were analyzed using SPSS version 20.0 for descriptive analysis and Mplusversion 7.4 for latent class analysis. Results: Four latent classes of risk factors for coronary artery disease were identified in the final model:‘smoking-drinking’, ‘high-risk for dyslipidemia’, ‘high-risk for metabolic syndrome’, and ‘high-risk for diabetes and malnutrition’. The likelihood ofthese latent classes varied significantly based on socio-demographic characteristics, including age, gender, educational level, and occupation. Conclusion: The results showed significant heterogeneity in the pattern of risk factors for coronary artery disease. These findings provide helpfuldata to develop intervention strategies for the effective prevention of coronary artery disease. Specific characteristics depending on the subpopulationshould be considered during the development of interventions.