Lifestyle and Biometrics Health Dataset

Dataset Features

• Demographic Information:
  • Sex: Male or Female
  • Age: Rounded up to the nearest 5 years
• Body Measurements:
  • Height: Rounded up to the nearest 5 cm (in cm)
  • Weight: Weight in kilograms (kg)
  • Waistline: Waist circumference in cm
• Vision and Hearing:
  • Sight Left / Sight Right: Eyesight measurements for each eye
  • Hearing Left / Hearing Right: Hearing capability for each ear (1 = Normal, 2 = Abnormal)
• Vital Signs:
  • Systolic Blood Pressure (SBP): Measured in mmHg
  • Diastolic Blood Pressure (DBP): Measured in mmHg
• Blood Tests:
  • Blood Glucose (BLDS): Fasting blood glucose levels (mg/dL)
  • Total Cholesterol: Total cholesterol level (mg/dL)
  • HDL Cholesterol: High-density lipoprotein level (mg/dL)
  • LDL Cholesterol: Low-density lipoprotein level (mg/dL)
  • Triglyceride: Triglyceride level (mg/dL)
  • Hemoglobin: Hemoglobin concentration (g/dL)
  • Serum Creatinine: Blood creatinine levels (mg/dL)
• Liver Enzymes:
  • SGOT/AST: Glutamate-oxaloacetate transaminase levels (IU/L)
  • SGPT/ALT: Alanine transaminase levels (IU/L)
  • Gamma-GTP: Gamma-glutamyl transferase levels (IU/L)
• Urine Protein:
  • Protein levels in urine (1 = -, 2 = ±, 3 = +1, 4 = +2, 5 = +3, 6 = +4)
• Health Behaviors:
  • Smoking Status: 1 = Never smoked, 2 = Quit smoking, 3 = Currently smoking
  • Drinking Status: Y = Yes (drinker), N = No (non-drinker)

Usage

• Health Risk Analysis: Investigate the impact of smoking and drinking on various body metrics such as blood pressure, liver enzymes, and cholesterol levels.
• Predictive Modeling: Develop models to predict smoking and drinking status based on physiological data and blood test results.
• Healthcare Research: Study the correlations between health behaviors and body signals, which can aid in developing preventive health programs.

License

• License: Restricted to non-commercial use
• Data Volume: Moderate size, suitable for machine learning and statistical analysis

Who can use it:

• Researchers and Data Scientists: For building classification models and conducting statistical analysis on health behavior impacts.
• Public Health Officials and Policymakers: To inform health interventions and campaigns aimed at reducing smoking and drinking rates.
• Healthcare Providers: To better understand body metrics associated with lifestyle risks and improve patient care.

How to Use This Dataset

1. Classification Modeling: Create algorithms to classify individuals as smokers or drinkers based on their body metrics and blood test data.
2. Trend Analysis: Explore patterns in body metrics across different age groups, sexes, and health behaviors.
3. Health Program Evaluation: Assess health initiatives by understanding the physiological effects of lifestyle choices like smoking and drinking.