Objective To investigate the predictive factors of acute coronary syndrome (ACS) based on the information extracted from physical examination data, and to establish a logistic regression model and evaluate its accuracy, sensitivity and specificity in the prediction of ACS. Methods Clinical data of 100 identified ACS patients admitted to the outpatient, emergency and cardiologic departments of our hospital from October 2014 to October 2015 were collected and retrospectively reviewed in this study. Another 100 sex- and age-matched individuals without ACS who took physical examination in our hospital during the same period served as control group. The physical examination data were extracted from the database of physical examination center, and then analyzed with univariate analysis. The factors with statistical significance were further analyzed with multivariate logistic regression analysis to establish a model for ACS prediction. Results Univariate analysis showed that there were 15 clinical variables, including body mass index (BMI), uric acid (UA), total cholesterol (TC), low density lipoprotein-cholesterol (LDL-C), high sensitivity C-reactive protein (Hs-CRP), homocysteine (Hcy), hypertension, smoking, diabetes, hyperlipidemia, ischemic stroke, pulse wave velocity (PWV), thickening of intima-media thickness (IMT), antiplatelet therapy, and statins treament were associated with the occurrence of ACS. Among them, there were 8 factors into the logistic regression model, and the logistic regression model was P=1/1+e(-8.444+1.182X₁₊₁.174X₂₊₀.430X₃₊₀.323X₄₊₀.315X₅₊₀.257X₆-1.569X₇-0.184X₈), with the accuracy, sensitivity and specificity of 83.5% (167/200), 82% (82/100), and 85% (85/100), respectively, Conclusions IMT, diabetes, smoking, Hcy, LDL-C, BMI, antiplatelet therapy, and statins treatment are the main risk factors for ACS. Our established logistic regression model is of good predictive value for the risk of ACS.