Research on the Influencing Factors of Heart Disease

Cardiovascular disease, particularly heart disease, remains a predominant contributor to global morbidity and mortality, highlighting the need for a greater understanding of its multifactorial etiology. This study systematically explores the principal determinants associated with heart disease through a comprehensive analysis of demographic, behavioral, and clinical variables. Employing robust statistical methodologies, including multivariate logistic regression and supervised machine learning algorithms, this paper evaluates the relative influence and interaction of variables such as age, sex, blood pressure, serum cholesterol, smoking status, diabetes mellitus, and electrocardiographic abnormalities. The findings underscore the complex interplay between modifiable and non-modifiable risk factors, with age, hypertension, and diabetes emerging as the most significant predictors. These insights not only enhance the current epidemiological understanding of heart disease but also provide an empirical foundation for the development of predictive models and targeted intervention strategies. The study advocates for integrated, data-driven approaches in cardiovascular risk assessment and prevention, while highlighting the critical need for translating these computational insights into actionable clinical decision-support systems that can bridge the gap between risk prediction and personalized patient management.