Bridging the Gap in Hypertension Management: Evaluating Blood Pressure Control and Associated Risk Factors in a Resource-Constrained Setting
Description
Dataset Description This dataset contains a simulated collection of 10,000 patient records, designed to explore hypertension management in resource-constrained settings. It provides comprehensive data for analyzing blood pressure control rates, associated risk factors, and complications. The dataset is ideal for predictive modeling, risk analysis, and treatment optimization, offering insights into demographic, clinical, and treatment-related variables. Dataset Structure 1. Dataset Volume • Size: 10,000 records. • Features: 19 variables, categorized into Sociodemographic, Clinical, Complications, and Treatment/Control groups. 2. Variables and Categories A. Sociodemographic Variables 1. Age: • Continuous variable in years. • Range: 18–80 years. • Mean ± SD: 49.37 ± 12.81. 2. Sex: • Categorical variable. • Values: Male, Female. 3. Education: • Categorical variable. • Values: No Education, Primary, Secondary, Higher Secondary, Graduate, Post-Graduate, Madrasa. 4. Occupation: • Categorical variable. • Values: Service, Business, Agriculture, Retired, Unemployed, Housewife. 5. Monthly Income: • Categorical variable in Bangladeshi Taka. • Values: <5000, 5001–10000, 10001–15000, >15000. 6. Residence: • Categorical variable. • Values: Urban, Sub-urban, Rural. B. Clinical Variables 7. Systolic BP: • Continuous variable in mmHg. • Range: 100–200 mmHg. • Mean ± SD: 140 ± 15 mmHg. 8. Diastolic BP: • Continuous variable in mmHg. • Range: 60–120 mmHg. • Mean ± SD: 90 ± 10 mmHg. 9. Elevated Creatinine: • Binary variable (\geq 1.4 \, \text{mg/dL}). • Values: Yes, No. 10. Diabetes Mellitus: • Binary variable. • Values: Yes, No. 11. Family History of CVD: • Binary variable. • Values: Yes, No. 12. Elevated Cholesterol: • Binary variable (\geq 200 \, \text{mg/dL}). • Values: Yes, No. 13. Smoking: • Binary variable. • Values: Yes, No. C. Complications 14. LVH (Left Ventricular Hypertrophy): • Binary variable (ECG diagnosis). • Values: Yes, No. 15. IHD (Ischemic Heart Disease): • Binary variable. • Values: Yes, No. 16. CVD (Cerebrovascular Disease): • Binary variable. • Values: Yes, No. 17. Retinopathy: • Binary variable. • Values: Yes, No. D. Treatment and Control 18. Treatment: • Categorical variable indicating therapy type. • Values: Single Drug, Combination Drugs. 19. Control Status: • Binary variable. • Values: Controlled, Uncontrolled. Dataset Applications 1. Predictive Modeling: • Develop models to predict blood pressure control status using demographic and clinical data. 2. Risk Analysis: • Identify significant factors influencing hypertension control and complications. 3. Severity Scoring: • Quantify hypertension severity for patient risk stratification. 4. Complications Prediction: • Forecast complications like IHD, LVH, and CVD for early intervention. 5. Treatment Guidance: • Analyze therapy efficacy to recommend optimal treatment strategies.