Bayesian Analysis for Cardiovascular Risk Factors in Ischemic Heart Disease

Ischemic heart disease (or Coronary Artery Disease) is the most common cause of death in various countries, characterized by reduced blood supply to heart. Statistical models make an impact evaluating risk factors that are responsible for mortality and morbidity during IHD (Ischemic disease). In general, geometric or Poisson distributions can underestimate zero-count probability hence it difficult identify significant effects covariates improving conditions due regional wall motion abnormalities. this work, a flexible class zero inflated introduced. A Bayesian estimation method developed as alternative traditionally used maximum likelihood-based methods analyze such data. Simulation studies show proposed has better small sample performance than classical method, with tighter interval estimates coverage probabilities. Although prevention CAD long been focus public health policy, clinical medicine, biomedical scientific investigation, prevalence remains high despite current strategies treatment. Various comprehensive searches have performed MEDLINE, HealthSTAR, Global Health databases providing insights into traditional emerging CAD. real-life data set illustrated using WinBUGS.