| # README — Cardiovascular Heart Failure Dataset |
|
|
| **File:** `cardiovascular_hf_dataset-3.csv` |
| **Rows:** 50,000 patients |
| **Columns:** 68 features |
| **Domain:** Cardiovascular medicine — Heart Failure risk prediction |
|
|
| ----- |
|
|
| ## Overview |
|
|
| This dataset contains multimodal patient-level data for heart failure (HF) risk modelling. Each row represents one patient encounter with demographic, clinical, laboratory, echocardiographic, symptomatic, pharmacological, and longitudinal variables. Three outcome targets are provided: a binary HF risk label, a future hospitalisation probability, and a 12-month survival probability. |
|
|
| ----- |
|
|
| ## Data Sources |
|
|
| |Source |N |% | |
| |---------------------|------|-----| |
| |Preventive_Cardiology|20,094|40.2%| |
| |Hospital_Inpatient |15,052|30.1%| |
| |Annual_Checkup |14,854|29.7%| |
| |
| ----- |
| |
| ## Column Dictionary |
| |
| ### 1. Patient Identifiers |
| |
| |Column |Type|Description | |
| |------------|----|-----------------------------------------------| |
| |`Patient_ID`|str |Unique patient identifier (PT000001 – PT050000)| |
| |`Source` |str |Clinical setting of data collection | |
|
|
| ----- |
|
|
| ### 2. Demographics |
|
|
| |Column |Type|Range / Categories |Description | |
| |-----------|----|-----------------------------------------------------|-----------------------| |
| |`Age` |int |18 – 90 |Age in years | |
| |`Gender` |str |Female, Male, Other |Self-reported gender | |
| |`Ethnicity`|str |White, Black, Hispanic, Asian, Native_American, Other|Self-reported ethnicity| |
| |
| ----- |
| |
| ### 3. Lifestyle & Socioeconomic |
| |
| |Column |Type |Range |Description | |
| |----------------------------|-----|---------------------------------------------|---------------------------------------------------------| |
| |`BMI` |float|14.0 – 58.0 |Body mass index (kg/m²) | |
| |`Activity_Level` |str |Sedentary / Low / Moderate / High / Very_High|Self-reported physical activity category | |
| |`SES_Score` |float|1.0 – 10.0 |Socioeconomic status composite score | |
| |`SES_Category` |str |Low / Middle / High |Derived SES category | |
| |`Smoking_Status` |str |Never / Former / Current |Tobacco smoking history | |
| |`Alcohol_Units_Week` |float|0.2 – 50.0 |Alcohol consumption (units/week) | |
| |`Physical_Activity_Min_Week`|float|0 – 448 |Weekly moderate-intensity exercise (minutes); ~5% missing| |
| |`Sleep_Hours` |float|3.0 – 11.9 |Average nightly sleep duration; ~4.9% missing | |
| |`Diet_Score` |float|1.0 – 10.0 |Dietary quality composite score; ~5% missing | |
| |`Stress_Score` |float|1.0 – 10.0 |Perceived stress score; ~4.9% missing | |
|
|
| ----- |
|
|
| ### 4. Comorbidities (binary flags: 0 = absent, 1 = present) |
|
|
| |Column |Prevalence| |
| |----------------------------------|----------| |
| |`Hypertension` |44.8% | |
| |`Diabetes` |28.4% | |
| |`CAD` (Coronary Artery Disease) |23.9% | |
| |`Prior_MI` (Myocardial Infarction)|4.9% | |
| |`Arrhythmia` |19.8% | |
| |`CKD` (Chronic Kidney Disease) |20.5% | |
| |`Family_HF_History` |24.0% | |
|
|
| ----- |
|
|
| ### 5. Vital Signs |
|
|
| |Column |Type |Range |Description | |
| |------------------|-----|------------|--------------------------------| |
| |`Heart_Rate_bpm` |int |38 – 135 |Resting heart rate (bpm) | |
| |`SBP_mmHg` |int |72 – 210 |Systolic blood pressure (mmHg) | |
| |`DBP_mmHg` |int |43 – 132 |Diastolic blood pressure (mmHg) | |
| |`Respiratory_Rate`|int |8 – 33 |Respiratory rate (breaths/min) | |
| |`SpO2_pct` |float|87.7 – 100.0|Peripheral oxygen saturation (%)| |
|
|
| ----- |
|
|
| ### 6. Biomarkers & Laboratory Values (~9–10% missing) |
|
|
| |Column |Unit |Description | |
| |--------------------|------|-------------------------------------------------| |
| |`BNP_pgmL` |pg/mL |B-type natriuretic peptide — primary HF biomarker| |
| |`NT_proBNP_pgmL` |pg/mL |N-terminal pro-BNP — HF severity marker | |
| |`Troponin_I_ngmL` |ng/mL |Cardiac troponin I — myocardial injury marker | |
| |`HbA1c_pct` |% |Glycated haemoglobin — glycaemic control | |
| |`LDL_mgdL` |mg/dL |Low-density lipoprotein cholesterol | |
| |`HDL_mgdL` |mg/dL |High-density lipoprotein cholesterol | |
| |`Triglycerides_mgdL`|mg/dL |Serum triglycerides | |
| |`Creatinine_mgdL` |mg/dL |Serum creatinine — renal function | |
| |`eGFR_mLmin` |mL/min|Estimated glomerular filtration rate | |
| |`Sodium_mEqL` |mEq/L |Serum sodium — electrolyte | |
| |`Potassium_mEqL` |mEq/L |Serum potassium — electrolyte | |
| |`CRP_mgL` |mg/L |C-reactive protein — systemic inflammation | |
|
|
| ----- |
|
|
| ### 7. Electrocardiographic (ECG) Variables |
|
|
| |Column |Type |Description | |
| |-----------------|------|-------------------------------------------| |
| |`QTc_Interval_ms`|int |Corrected QT interval (ms); range 340–574 | |
| |`QRS_Duration_ms`|int |QRS complex duration (ms); range 60–171 | |
| |`ST_Abnormality` |binary|ST-segment abnormality present (22.0%) | |
| |`LVH_Flag` |binary|Left ventricular hypertrophy on ECG (29.4%)| |
| |`AF_Presence` |binary|Atrial fibrillation detected (26.5%) | |
|
|
| ----- |
|
|
| ### 8. Echocardiographic Variables (~10% missing) |
|
|
| |Column |Unit |Description | |
| |--------------------------|------|---------------------------------------------| |
| |`Ejection_Fraction_pct` |% |Left ventricular ejection fraction (15–80%) | |
| |`LV_Wall_Thickness_mm` |mm |Left ventricular wall thickness (6–18.4 mm) | |
| |`Cardiomegaly` |binary|Cardiomegaly present (25.6%) | |
| |`Echo_Abnormalities_Score`|0–5 |Composite echocardiographic abnormality score| |
|
|
| ----- |
|
|
| ### 9. Symptoms (binary flags) |
|
|
| |Column |Prevalence|Description | |
| |----------------------|----------|-------------------------------------------| |
| |`Dyspnea` |35.4% |Shortness of breath | |
| |`Fatigue` |36.2% |Fatigue or generalised weakness | |
| |`Chest_Discomfort` |29.1% |Chest pain or pressure | |
| |`Peripheral_Edema` |31.9% |Ankle/leg swelling | |
| |`Exercise_Intolerance`|36.3% |Reduced exercise capacity | |
| |`Dizziness` |21.1% |Lightheadedness or dizziness | |
| |`Symptom_Burden_Count`|int (0–6) |Total number of symptoms present (mean 1.9)| |
|
|
| ----- |
|
|
| ### 10. Medications (binary flags: 0 = not prescribed, 1 = prescribed) |
|
|
| |Column |Prevalence| |
| |---------------|----------| |
| |`ACE_Inhibitor`|30.5% | |
| |`Beta_Blocker` |33.9% | |
| |`Diuretic` |28.6% | |
| |`Statin` |37.0% | |
| |`Anticoagulant`|28.1% | |
|
|
| ----- |
|
|
| ### 11. Healthcare Utilisation |
|
|
| |Column |Type|Range|Description | |
| |------------------------|----|-----|--------------------------------------------| |
| |`Prior_Hospitalizations`|int |0 – 9|Number of prior hospital admissions | |
| |`ED_Visits_Per_Year` |int |0 – 9|Emergency department visits in the past year| |
|
|
| ----- |
|
|
| ### 12. Longitudinal / Trend Variables (~17–18% missing) |
|
|
| |Column |Unit |Description | |
| |---------------------------|----------|----------------------------------------------------------------| |
| |`Weight_Fluctuation_3mo_kg`|kg |Weight change over 3 months (− = loss, + = gain) | |
| |`BP_Trend_mmHg_month` |mmHg/month|Blood pressure trend over recent months | |
| |`HRV_ms` |ms |Heart rate variability (5–88 ms; higher = better autonomic tone)| |
|
|
| ----- |
|
|
| ### 13. Outcome Variables |
|
|
| |Column |Type |Description | |
| |-----------------------------|-----------|---------------------------------------------------| |
| |`HF_Risk_Binary` |0 / 1 |Primary label — heart failure risk (26.1% positive)| |
| |`Future_Hospitalization_Risk`|float [0–1]|Predicted probability of future hospitalisation | |
| |`Survival_Probability_12mo` |float [0–1]|Predicted probability of survival at 12 months | |
|
|
| **Class imbalance note:** HF positive cases represent 26.1% of the dataset. Consider SMOTE, class weighting, or stratified sampling before model training. |
|
|
| ----- |
|
|
| ## Missing Data Summary |
|
|
| |Missingness tier |Columns |Approx. missing| |
| |-----------------|---------------------------------------------|---------------| |
| |Low (~5%) |Physical activity, sleep, diet, stress scores|4.9–5.0% | |
| |Moderate (~9–10%)|All biomarkers, echocardiographic variables |9.2–10.4% | |
| |High (~17–18%) |Weight fluctuation, BP trend, HRV |17.6–18.1% | |
|
|
| Recommended imputation strategies: median imputation or MICE for continuous variables; mode imputation for binary flags. Missing echo variables may reflect patients who did not undergo echocardiography (MCAR/MAR assumption should be validated). |
|
|
| ----- |
|
|
| ## Suggested Use Cases |
|
|
| - Binary classification: HF risk prediction (`HF_Risk_Binary`) |
| - Regression: 12-month survival probability or future hospitalisation risk |
| - Survival analysis: time-to-event modelling using `Survival_Probability_12mo` |
| - Missing data benchmarking: graduated missingness across feature tiers |
| - Multi-source generalisation studies: three distinct clinical settings |
| - Feature importance analysis: identifying key biomarker and symptom drivers of HF |
|
|
| ----- |
|
|
| ## Notes |
|
|
| - All Patient_IDs are unique (no duplicate rows). |
| - The dataset is synthetic or de-identified; do not attempt patient re-identification. |
| - Echocardiographic variables (`Ejection_Fraction_pct`, `LV_Wall_Thickness_mm`, `Cardiomegaly`, `Echo_Abnormalities_Score`) share a correlated missingness pattern, suggesting they are collected together as part of an echo workup. |
| - BNP and NT-proBNP are highly correlated; consider using only one in models to avoid multicollinearity. |
| - Age range spans 18–90 years; consider age-stratified analyses for paediatric-excluded adult cohorts. |
|
|
| ----- |
|
|
| *README generated for `cardiovascular_hf_dataset-3.csv` — 50,000 rows × 68 columns.* |
