Predictors of Cardiovascular Disease Among Adults: A Multivariable Logistic Regression Analysis

Cardiovascular disease remains the leading cause of morbidity and mortality worldwide. Early identification of individuals at high risk of heart disease is essential for effective prevention and clinical intervention. Routinely collected demographic and clinical variables may offer valuable predictive insight when evaluated using appropriate statistical models. This study aimed to examine whether age, sex, cholesterol level, and exercise-induced angina significantly predict the likelihood of heart disease among adults using a publicly available heart failure dataset. A secondary analysis was conducted using data from 918 adults obtained from the Kaggle Heart Failure Dataset. Heart disease status (yes/no) was modeled as a binary outcome. Multivariable logistic regression was used to assess associations between predictors and heart disease. Model assumptions were evaluated using variance inflation factors, Box–Tidwell tests for linearity of the logit, influence diagnostics, and goodness-of-fit statistics. Discriminatory performance was assessed using receiver operating characteristic analysis. The logistic regression model significantly improved prediction of heart disease compared with the intercept-only model (likelihood ratio χ²₄=370.14, P <.001). The model demonstrated strong discrimination (area under the curve = 0.846) and good calibration (Hosmer–Lemeshow P =.667). All predictors were statistically significant ( P <.001). Increasing age was associated with higher odds of heart disease (odds ratio [OR] = 1.051, 95% CI 1.032–1.070). Males had substantially higher odds than females (OR = 3.494, 95% CI 2.299–5.309). Exercise-induced angina was the strongest predictor (OR = 9.911, 95% CI 6.873–14.293). Cholesterol level showed a statistically significant but inverse association with heart disease (OR = 0.995, 95% CI 0.994–0.997). Age, sex, cholesterol level, and exercise-induced angina were significant predictors of heart disease in this dataset. Exercise-induced angina and male sex demonstrated particularly strong associations. These findings highlight the value of simple, routinely collected clinical indicators for heart disease risk stratification and support the use of logistic regression as an effective analytical approach in population-level cardiovascular research.