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The Cardiovascular Risk Prediction dataset aims to predict the 10-year risk of developing coronary heart disease using 15 attributes, offering insights into risk factors and aiding in prevention and early detection strategies.

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Cardiovascular-Risk-Prediction

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The Cardiovascular Risk Prediction dataset is a classification machine learning project based on an ongoing cardiovascular study on residents of the town of Framingham, Massachusetts. The dataset contains over 4000 records and 15 attributes, including demographic, behavioral, and medical risk factors. The goal of the project is to predict whether the patient has a 10-year risk of developing coronary heart disease (CHD). The attributes in the dataset include age, education level, gender, smoking status, blood pressure, cholesterol levels, diabetes status, body mass index, and heart rate, among others.

The dataset provides a unique opportunity for researchers and healthcare professionals to better understand the risk factors for cardiovascular disease and develop more effective prevention and early detection strategies. By analyzing the relationships between the various risk factors and the development of CHD, predictive models can be developed to accurately estimate an individual's risk of developing this disease over a 10-year period.

Id : A unique identifier for each individual

age: Age of the individual in years

education: Level of education of the individual (categorical variable)

sex: Gender of the individual (binary variable)

is_smoking: Whether the individual is a smoker or not (binary variable)

cigsPerDay: Number of cigarettes smoked per day (continuous variable)

BPMeds: Whether the individual is on blood pressure medication or not (binary variable)

prevalentStroke: Whether the individual has had a stroke in the past (binary variable)

prevalentHyp: Whether the individual has hypertension (high blood pressure) or not (binary variable)

diabetes: Whether the individual has diabetes or not (binary variable)

totChol: Total cholesterol levels of the individual (continuous variable)

sysBP: Systolic blood pressure of the individual (continuous variable)

diaBP: Diastolic blood pressure of the individual (continuous variable)

BMI: Body Mass Index of the individual (continuous variable)

heartRate: Heart rate of the individual (continuous variable)

glucose: Blood glucose level of the individual (continuous variable)

TenYearCHD: The 10-year risk of developing coronary heart disease for the individual (binary variable)

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The Cardiovascular Risk Prediction dataset aims to predict the 10-year risk of developing coronary heart disease using 15 attributes, offering insights into risk factors and aiding in prevention and early detection strategies.

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