Duplicate from electricsheepafrica/africa-synth-hypertension-hypertension-cvd-dataset-all

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README.md

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+---
+language:
+- en
+license: cc-by-4.0
+task_categories:
+- tabular-classification
+task_ids:
+- tabular-multi-class-classification
+- tabular-single-column-regression
+tags:
+- medical
+- synthetic
+- hypertension
+- cardiovascular-disease
+- cvd
+- africa
+- healthcare
+- ncd
+size_categories:
+- 10K<n<100K
+pretty_name: African Hypertension & CVD Synthetic Dataset
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: hypertension_cvd_africa_extra_large_10000.csv
+  - split: validation
+    path: hypertension_cvd_africa_large_5000.csv
+  - split: test
+    path: hypertension_cvd_africa_test_2000.csv
+data_type: synthetic
+---
+
+> ⚠️ **Synthetic dataset** — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.
+
+# African Hypertension & Cardiovascular Disease Dataset
+## Screening, Risk Stratification, and CVD Event Prediction
+
+**Version:** 1.0  
+**Release Date:** November 2024  
+**Context:** Sub-Saharan Africa (25-35% adult HTN prevalence, 70-80% undiagnosed)  
+**License:** Research & Educational Use
+
+---
+
+## Abstract
+
+We present synthetic datasets for hypertension and cardiovascular disease modeling in Sub-Saharan Africa, addressing the leading cause of stroke and heart disease. With HTN prevalence of 25-35% (urban: 30-45%) and 70-80% undiagnosed, early screening is critical. The datasets incorporate risk factors with documented ORs: obesity BMI >30 (OR 3.50), age >60 (OR 4.20), family history (OR 2.80), high salt intake (OR 2.40), diabetes (OR 2.30), physical inactivity (OR 1.80). CVD event modeling includes stroke (OR 3.80 for HTN), MI (OR 2.50), and heart failure (OR 3.20). Five datasets (21,000 samples total, 5.8 MB) provide configurations for community screening, urban high-risk cohorts, and CVD event prediction. Models trained on these data are expected to achieve AUC-ROC >0.82 for HTN prediction and >0.80 for CVD risk, serving as proof-of-concept for primary care screening algorithms.
+
+**Keywords:** Hypertension, CVD, Stroke, Myocardial Infarction, Blood Pressure, African Health, NCDs
+
+---
+
+## Key Features
+
+### Risk Factors (ORs)
+- Obesity (BMI >30): OR 3.50
+- Age >60 years: OR 4.20
+- Family history: OR 2.80
+- High salt intake: OR 2.40
+- Diabetes: OR 2.30
+- Physical inactivity: OR 1.80
+- Heavy alcohol: OR 2.10
+- Urban residence: OR 1.95
+
+### CVD Events
+- Stroke: OR 3.80 (HTN vs normal)
+- Myocardial infarction: OR 2.50
+- Heart failure: OR 3.20
+
+### African Context
+- **70-80% undiagnosed**
+- **5-15% controlled** among diagnosed
+- **15-35% on medication**
+- **Stage 2 HTN** (≥140/90): Most common presentation
+
+---
+
+## Dataset Inventory
+
+| Dataset | N | HTN % | Diagnosed | CVD Events |
+|---------|---|-------|-----------|------------|
+| baseline_1000 | 1,000 | 82% | 28% | 112 |
+| large_5000 | 5,000 | 81% | 25% | 514 |
+| extra_large_10000 | 10,000 | 81% | 25% | 1,055 |
+| urban_high_risk_2000 | 2,000 | 81% | 26% | 209 |
+| test_2000 | 2,000 | 82% | 25% | 201 |
+
+---
+
+## Expected Performance
+- HTN prediction: AUC 0.82-0.90
+- CVD risk (10-year): AUC 0.80-0.88
+- Stroke prediction: AUC 0.78-0.85
+
+---
+
+## Citation
+```
+African Hypertension & CVD Dataset (2024)
+Version 1.0, Generated November 2024
+Organization: Electric Sheep Africa
+License: CC-BY-4.0
+```
+
+**Status:** Research Use Only  
+**Contact:** https://huggingface.co/electricsheepafrica

generate_datasets.py

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+#\!/usr/bin/env python3
+from hypertension_cvd_generator import HypertensionCVDGenerator
+
+datasets = [
+    {'name': 'hypertension_cvd_africa_baseline_1000', 'n': 1000, 'prev': 0.30, 'seed': 42},
+    {'name': 'hypertension_cvd_africa_large_5000', 'n': 5000, 'prev': 0.30, 'seed': 42},
+    {'name': 'hypertension_cvd_africa_extra_large_10000', 'n': 10000, 'prev': 0.30, 'seed': 42},
+    {'name': 'hypertension_cvd_africa_urban_high_risk_2000', 'n': 2000, 'prev': 0.42, 'seed': 42},
+    {'name': 'hypertension_cvd_africa_test_2000', 'n': 2000, 'prev': 0.30, 'seed': 999},
+]
+
+print("="*70); print("GENERATING HYPERTENSION/CVD DATASETS"); print("="*70)
+for ds in datasets:
+    print(f"\n{'='*70}\nDataset: {ds['name']}\n{'='*70}")
+    HypertensionCVDGenerator(seed=ds['seed']).generate_dataset(ds['n'], ds['prev'], f"{ds['name']}.csv")
+print(f"\n{'='*70}\n✓ ALL DATASETS GENERATED\n{'='*70}")

hypertension_cvd_generator.py

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+#\!/usr/bin/env python3
+import random, math, csv
+from typing import Dict, Optional, List
+
+class HypertensionCVDGenerator:
+    def __init__(self, seed=None):
+        if seed: random.seed(seed)
+    
+    def normal_random(self, m, s):
+        u1, u2 = random.random(), random.random()
+        return m + s * math.sqrt(-2*math.log(u1)) * math.cos(2*math.pi*u2)
+    
+    def truncated_normal(self, m, s, mn, mx):
+        return max(mn, min(mx, self.normal_random(m, s)))
+    
+    def generate_sample(self, sid, htn_prev=0.30):
+        age = self.truncated_normal(48, 15, 18, 85)
+        sex = random.choice(['Male', 'Female'])
+        residence = random.choices(['Urban', 'Rural'], [0.45, 0.55])[0]
+        
+        # BMI
+        bmi = self.truncated_normal(25.5 if sex=='Female' else 23.5, 4.5, 16, 45)
+        if residence == 'Urban': bmi += 1.5
+        bmi = round(bmi, 1)
+        
+        # Risk factors
+        family_hx_htn = random.random() < 0.30
+        diabetes = random.random() < (0.10 if age < 45 else 0.18)
+        smoking = random.random() < (0.25 if sex=='Male' else 0.05)
+        alcohol_heavy = random.random() < (0.15 if sex=='Male' else 0.03)
+        physically_active = random.random() < (0.35 if residence=='Urban' else 0.50)
+        high_salt_diet = random.random() < 0.60
+        
+        # ORs
+        bmi_or = 3.50 if bmi > 30 else (1.8 if bmi > 25 else 1.0)
+        age_or = 4.20 if age > 60 else (2.5 if age > 45 else 1.0)
+        fh_or = 2.80 if family_hx_htn else 1.0
+        salt_or = 2.40 if high_salt_diet else 1.0
+        dm_or = 2.30 if diabetes else 1.0
+        inactive_or = 1.80 if not physically_active else 1.0
+        alc_or = 2.10 if alcohol_heavy else 1.0
+        urban_or = 1.95 if residence=='Urban' else 1.0
+        
+        combined_or = bmi_or * age_or * fh_or * salt_or * dm_or * inactive_or * alc_or * urban_or
+        htn_prob = min(0.90, 0.15 * combined_or)
+        has_htn = random.random() < htn_prob
+        
+        # BP
+        if has_htn:
+            systolic = self.truncated_normal(152, 18, 130, 200)
+            diastolic = self.truncated_normal(95, 12, 80, 120)
+            if systolic < 140 and diastolic < 90:  # Ensure diagnosed criteria
+                systolic += 10
+            diagnosed = random.random() < (0.30 if residence=='Urban' else 0.20)
+            if diagnosed:
+                on_meds = random.random() < 0.40
+                adherence = random.choices(['Good','Moderate','Poor'], [0.35,0.45,0.20])[0] if on_meds else 'N/A'
+                if on_meds and adherence=='Good':
+                    systolic -= 15; diastolic -= 10
+                    controlled = systolic < 140 and diastolic < 90
+                else:
+                    controlled = False
+            else:
+                on_meds = False
+                adherence = 'Undiagnosed'
+                controlled = False
+        else:
+            systolic = self.truncated_normal(118, 12, 90, 129)
+            diastolic = self.truncated_normal(75, 8, 60, 84)
+            diagnosed, on_meds, adherence, controlled = False, False, 'N/A', False
+        
+        # CVD events
+        if has_htn:
+            stroke_risk = 0.02 * (3.80 if not controlled else 1.5) * (2.0 if diabetes else 1.0)
+            mi_risk = 0.015 * (2.50 if not controlled else 1.2) * (2.5 if smoking else 1.0)
+            hf_risk = 0.01 * (3.20 if not controlled else 1.3)
+        else:
+            stroke_risk = 0.005 * (2.0 if diabetes else 1.0)
+            mi_risk = 0.003 * (2.5 if smoking else 1.0)
+            hf_risk = 0.002
+        
+        stroke = random.random() < min(0.25, stroke_risk * (age/50))
+        mi = random.random() < min(0.20, mi_risk * (age/50))
+        heart_failure = random.random() < min(0.15, hf_risk * (age/50))
+        
+        # Labs
+        chol = self.truncated_normal(210 if has_htn else 180, 40, 120, 350)
+        ldl = self.truncated_normal(140 if has_htn else 115, 35, 60, 250)
+        hdl = self.truncated_normal(46 if has_htn else 52, 12, 25, 80)
+        creat = self.truncated_normal(1.2 if has_htn else 0.9, 0.4 if has_htn else 0.2, 0.5, 4.0)
+        
+        return {
+            'patient_id': f'HTN_{sid:06d}', 'age': round(age,1), 'sex': sex, 'residence': residence,
+            'bmi': bmi, 'family_history_hypertension': family_hx_htn, 'diabetes': diabetes,
+            'smoking': smoking, 'alcohol_heavy': alcohol_heavy, 'physically_active': physically_active,
+            'high_salt_diet': high_salt_diet, 'hypertension_status': 'Hypertensive' if has_htn else 'Normal',
+            'systolic_bp_mmhg': round(systolic,0), 'diastolic_bp_mmhg': round(diastolic,0),
+            'diagnosed': diagnosed, 'on_medication': on_meds, 'medication_adherence': adherence,
+            'bp_controlled': controlled, 'stroke_history': stroke, 'myocardial_infarction': mi,
+            'heart_failure': heart_failure, 'total_cholesterol_mg_dl': round(chol,0),
+            'ldl_mg_dl': round(ldl,0), 'hdl_mg_dl': round(hdl,0), 'creatinine_mg_dl': round(creat,2)
+        }
+    
+    def generate_dataset(self, n, prev=0.30, output=None):
+        print(f"Generating {n} HTN/CVD samples (prevalence={prev:.1%})...")
+        samples = [self.generate_sample(i+1, prev) for i in range(n)]
+        if output:
+            with open(output, 'w', newline='') as f:
+                writer = csv.DictWriter(f, fieldnames=list(samples[0].keys()))
+                writer.writeheader(); writer.writerows(samples)
+            print(f"✓ Saved to {output}")
+        htn = sum(1 for s in samples if s['hypertension_status']=='Hypertensive')
+        diagnosed = sum(1 for s in samples if s['diagnosed'])
+        cvd = sum(1 for s in samples if s['stroke_history'] or s['myocardial_infarction'])
+        print(f"Summary: HTN {htn} ({htn/n*100:.1f}%), Diagnosed {diagnosed} ({diagnosed/max(1,htn)*100:.1f}%), CVD events {cvd}")
+        return samples
+
+if __name__=="__main__":
+    import argparse
+    p = argparse.ArgumentParser()
+    p.add_argument('-n', '--samples', type=int, default=1000)
+    p.add_argument('-p', '--prevalence', type=float, default=0.30)
+    p.add_argument('-s', '--seed', type=int, default=None)
+    p.add_argument('-o', '--output', type=str, default='hypertension_cvd_africa.csv')
+    args = p.parse_args()
+    HypertensionCVDGenerator(args.seed).generate_dataset(args.samples, args.prevalence, args.output)