This study examined the relationship between Body mass index (BMI) classification and the occurrence of dyslipidemia using 3 years of health examination data from a university hospital in Changwon, and seeked to find other factors that affect dyslipidemia. Multivariate logistic regression analysis investigated the most common risk factors for developing dyslipidemia and used Kaplan-Meier survival curves to predict the probability of developing dyslipidemia according to BMI class. We then analyzed the effects of metabolic indicators such as smoking habits, alcohol consumption, blood pressure, glucose, hemoglobin A1c (HbA1c), and insulin. The Type I error rate was controlled through Bonferroni correction for multiple comparisons to extract reliable statistical data. in the present study, these results showed that the probability of developing dyslipidemia was high in the obese group, and confirmed that lifestyle habits such as smoking and drinking were highly correlated with the occurrence of dyslipidemia. Therefore, we suggest that dyslipidemia management and prevention strategies require public health policies that comprehensively manage lifestyle factors such as gender and weight management, smoking cessation, and drinking habits.