About Dataset

Context

• This dataset is from kaggle, access https://www.kaggle.com/datasets/alexteboul/heart-disease-health-indicators-dataset/data for more information

• Heart Disease is among the most prevalent chronic diseases in the United States, impacting millions of Americans each year and exerting a significant financial burden on the economy. In the United States alone, heart disease claims roughly 647,000 lives each year — making it the leading cause of death. The buildup of plaques inside larger coronary arteries, molecular changes associated with aging, chronic inflammation, high blood pressure, and diabetes are all causes of and risk factors for heart disease.

• While there are different types of coronary heart disease, the majority of individuals only learn they have the disease following symptoms such as chest pain, a heart attack, or sudden cardiac arrest. This fact highlights the importance of preventative measures and tests that can accurately predict heart disease in the population prior to negative outcomes like myocardial infarctions (heart attacks) taking place.

• The Centers for Disease Control and Prevention has identified high blood pressure, high blood cholesterol, and smoking as three key risk factors for heart disease. Roughly half of Americans have at least one of these three risk factors. The National Heart, Lung, and Blood Institute highlights a wider array of factors such as Age, Environment and Occupation, Family History and Genetics, Lifestyle Habits, Other Medical Conditions, Race or Ethnicity, and Sex for clinicians to use in diagnosing coronary heart disease. Diagnosis tends to be driven by an initial survey of these common risk factors followed by bloodwork and other tests.

Objective:

• Enhance and learn types of regression applyed in a health context.

Used Models:

• Linear Regression
• Decision Tree Regression
• Random Forest Regression
• Neural Networks Regression