study examines heart disease risk factors using SCAD-penalized quantile , to capture heterogeneous covariate effects across different of disease severity while achieving effective variable selection. Unlike mean- regression models, the approach allows regression coefficients to vary quantiles of the response , providing a detailed characterization of how predictors influence mild, , and severe forms of heart disease. The framework quantile regression with the smoothly clipped deviation penalty and is through a local linear approximation algorithm. analysis is conducted on real data obtained from a publicly available heart dataset. The empirical results pronounced distributional heterogeneity in severity and highlight clear -dependent patterns. Age and ST show consistently positive and effects toward higher quantiles, indiating stronger associations among patients severe disease, while maximum rate exhibits a stable protective effect across quantiles. Other predictors, incuding resting blood pressure, serum cholesterol, exercise-induced angina, mainly at upper quantiles, suggesting their primarily for severe . Overall, the findings demonstrate that SCAD- quantile regression provides a and interpretable framework for identifying meaningful heart disease factors and uncovering heterogeneity that is not using conventional methods.