Identification of Risk Factors for Heart Disease Based on Logistic Regression Model

Abstract

Objective. Accurate identification of independent heart disease contributors supports targeted regional prevention strategies. This study aims to examine associations between demographic, lifestyle and clinical factors (smoking, alcohol consumption, age, sex, diabetes mellitus, hypertension, body mass index) and heart disease prevalence in a hospitalized Anhui population. Methods. A hospital-based cross-sectional investigation was carried out among patients receiving inpatient care at the First Affiliated Hospital of Anhui Medical University. We systematically collected data covering demographic characteristics, daily lifestyle habits, past medical history, and physical examination parameters for all enrolled subjects. Multivariable unconditional logistic regression analysis was employed to quantify the independent correlations between the included variables and heart disease occurrence. Results. After adjustment for potential confounding factors, age (OR = 1.027, 95% CI: 1.005–1.050, P = 0.0178), hypertension (OR = 4.970, 95% CI: 2.255–11.58, P < 0.0001), diabetes mellitus (OR = 5.124, 95% CI: 1.889–15.91, P = 0.0010), family history (OR = 8.488, 95% CI: 2.726–33.22, P < 0.0001), smoking history (OR = 5.700, 95% CI: 2.228–15.81, P = 0.0002), alcohol consumption history (OR = 4.316, 95% CI: 2.049–9.501, P < 0.0001), and obesity (OR = 1.111, 95% CI: 1.018–1.217, P = 0.0180) were identified as independent risk factors for heart disease. Conclusion. Most of the confirmed risk factors are modifiable through behavioral adjustment and standardized clinical intervention. Comprehensive prevention and control measures targeting these factors are of great practical value for reducing the local heart disease burden in central China.