Despite significant advancements in pharmaceuticals and medical interventions, heart disease remains the leading cause of death worldwide. Although much research has been dedicated to understanding its risk factors, significant gaps persist, particularly in understudied populations. Addressing these gaps is essential to developing more effective, personalized strategies for preventing and treating heart disease. Current research often analyzes risk factors independently, without considering their potential interactions. As a result, treatments that benefit one patient may be ineffective for another, yet providers frequently adopt a one-size-fits-all approach. We can move toward a more personalized healthcare model by analyzing how risk factors, outcomes, diagnostics, and treatments vary between individuals. Understanding the complex interplay between genetic, social, and psychological determinants will help improve outcomes and bring us closer to truly individualized care on a global scale. This study aims to investigate factors that predict or protect against myocardial infarction (MI) in patients with a known risk factor of hyperlipidemia. Using regression modeling and subsets, the analysis will evaluate individual variables and their interactions.