This dataset is publicly available on Kaggle and originates from an ongoing cardiovascular study conducted in Framingham, Massachusetts. The primary objective is to predict whether a patient is at risk of developing coronary heart disease (CHD) over the next 10 years. The dataset comprises patient information, containing more than 4,000 records and 15 distinct attributes. These attributes encompass demographic, behavioral, and medical factors, each of which plays a role in assessing the risk of CHD.
- Sex: Gender of the patient (Nominal - male or female).
- Age: Age of the patient (Continuous - although ages are recorded as whole numbers, age is inherently continuous).
- Current Smoker: Indicates whether the patient is a current smoker (Nominal).
- Cigs Per Day: Represents the average number of cigarettes smoked per day (Continuous - as any number of cigarettes can be consumed, including fractions).
- BP Meds: Indicates whether the patient is currently taking blood pressure medication (Nominal).
- Prevalent Stroke: Indicates whether the patient has previously had a stroke (Nominal).
- Prevalent Hyp: Indicates whether the patient is hypertensive (Nominal).
- Diabetes: Indicates whether the patient has diabetes (Nominal).
- Tot Chol: Total cholesterol level (Continuous).
- Sys BP: Systolic blood pressure (Continuous).
- Dia BP: Diastolic blood pressure (Continuous).
- BMI: Body Mass Index (Continuous).
- Heart Rate: Heart rate (Continuous - considered continuous in medical research due to a large number of possible values).
- Glucose: Glucose level (Continuous).
- 10-Year Risk of Coronary Heart Disease (CHD): A binary variable where "1" indicates a high risk of CHD, and "0" denotes a low risk (binary classification).
This dataset serves as a valuable resource for researchers and healthcare professionals seeking to understand and predict the risk factors associated with coronary heart disease. It encompasses a wide range of attributes that can be utilized for various analytical and predictive purposes.