Synthetic Metabolic Syndrome Patient Records Dataset

Dataset Features

• seqn: A unique identifier for each individual in the dataset.
• Age: Age of the individual (in years).
• Sex: Gender of the individual (Male/Female).
• Marital: Marital status of the individual (e.g., Married, Separated, etc.).
• Income: Annual income of the individual (in simulated currency units).
• Race: Race or ethnicity of the individual (e.g., White, Black, Mexican American, etc.).
• WaistCirc: Waist circumference (in cm), an indicator of central obesity.
• BMI: Body Mass Index, a measure of body fat based on height and weight.
• Albuminuria: Presence of albumin in urine (binary indicator, 0 for no, 1 for yes).
• UrAlbCr: Urinary albumin-to-creatinine ratio, a measure of kidney health.
• UricAcid: Uric acid levels (in mg/dL), used to assess gout risk and metabolic health.
• BloodGlucose: Blood glucose level (in mg/dL), an indicator of diabetes or prediabetes.
• HDL: High-density lipoprotein cholesterol level (in mg/dL), often referred to as "good cholesterol."
• Triglycerides: Triglyceride levels (in mg/dL), a measure of fat in the blood.
• MetabolicSyndrome: Presence of metabolic syndrome (Yes/No), based on a combination of criteria such as waist circumference, blood pressure, glucose, HDL, and triglycerides.

Distribution

Usage

• Metabolic Syndrome Prediction: Develop machine learning models to predict the presence of metabolic syndrome based on demographic and biochemical markers.
• Risk Factor Analysis: Identify key risk factors for metabolic syndrome, such as obesity, high glucose, or low HDL cholesterol.
• Public Health Research: Investigate correlations between socioeconomic status (e.g., income and marital status) and metabolic syndrome prevalence.
• Personalized Healthcare: Design intervention strategies tailored to individuals based on their metabolic health profile.
• Health Disparities: Explore health disparities among racial and ethnic groups to inform equitable healthcare policies.

Coverage

License

• Healthcare Professionals: To study metabolic syndrome trends and tailor interventions.
• Data Scientists: For practicing classification, regression, and clustering techniques in healthcare analytics.
• Public Health Analysts: To assess population-level metabolic health and inform policies.
• Researchers: To simulate the impact of lifestyle changes on metabolic health outcomes.