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8b50a57 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 | #!/usr/bin/env python3
"""Validation & Diagnostic Visualization for Road Traffic Injury Dataset."""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
SCENARIOS = ['trauma_centre', 'district_hospital', 'rural_health_centre']
def load_scenarios(data_dir='data'):
dfs = {}
for sc in SCENARIOS:
path = os.path.join(data_dir, f'rti_{sc}.csv')
if os.path.exists(path):
dfs[sc] = pd.read_csv(path)
return dfs
def make_report(dfs, output='validation_report.png'):
fig, axes = plt.subplots(4, 2, figsize=(16, 22))
fig.suptitle('Road Traffic Injury & Trauma — Validation Report',
fontsize=16, fontweight='bold', y=0.98)
df = dfs.get('district_hospital', list(dfs.values())[0])
colors = ['#2ecc71', '#f39c12', '#e74c3c']
# Panel 1: Mortality across scenarios
ax = axes[0, 0]
x = np.arange(len(SCENARIOS))
mort = [(dfs[sc]['outcome'] == 'died').mean() * 100 for sc in SCENARIOS if sc in dfs]
ax.bar(x, mort, color=colors, alpha=0.8)
ax.set_xticks(x)
ax.set_xticklabels(['Trauma Centre', 'District', 'Rural'], fontsize=9)
for i, v in enumerate(mort):
ax.text(i, v + 0.3, f'{v:.1f}%', ha='center', fontsize=10)
ax.set_ylabel('Mortality (%)')
ax.set_title('RTI Mortality (Africa: 26.6/100K)')
# Panel 2: Road user type
ax = axes[0, 1]
users = df['road_user_type'].value_counts()
u_colors = ['#e74c3c', '#3498db', '#f39c12', '#2ecc71', '#9b59b6', '#e67e22']
ax.pie(users.values,
labels=[u.replace('_', ' ').title() for u in users.index],
autopct='%1.1f%%', colors=u_colors[:len(users)],
startangle=90, textprops={'fontsize': 8})
ax.set_title('Road User Type (WHO: Pedestrians >50% Africa)')
# Panel 3: GCS vs mortality
ax = axes[1, 0]
gcs_cats = ['severe', 'moderate', 'mild']
gcs_mort = []
for g in gcs_cats:
sub = df[df['gcs_category'] == g]
gcs_mort.append((sub['outcome'] == 'died').mean() * 100 if len(sub) > 0 else 0)
g_colors = ['#e74c3c', '#f39c12', '#2ecc71']
ax.bar(range(3), gcs_mort, color=g_colors, alpha=0.8)
ax.set_xticks(range(3))
ax.set_xticklabels(['Severe (3-8)', 'Moderate (9-12)', 'Mild (13-15)'])
for i, v in enumerate(gcs_mort):
ax.text(i, v + 0.3, f'{v:.0f}%', ha='center', fontsize=9)
ax.set_ylabel('Mortality (%)')
ax.set_title('Mortality by GCS Category')
# Panel 4: Body region
ax = axes[1, 1]
regions = df['primary_body_region'].value_counts()
ax.barh(range(len(regions)), regions.values, color='#3498db', alpha=0.7)
ax.set_yticks(range(len(regions)))
ax.set_yticklabels([r.replace('_', ' ').title()[:15] for r in regions.index], fontsize=7)
ax.set_xlabel('Count')
ax.set_title('Primary Body Region (Head 28%, Extremity 32%)')
# Panel 5: Ambulance & golden hour
ax = axes[2, 0]
amb = [(dfs[sc]['transport_mode'] == 'ambulance').mean() * 100 for sc in SCENARIOS if sc in dfs]
gh = [dfs[sc]['within_golden_hour'].mean() * 100 for sc in SCENARIOS if sc in dfs]
w = 0.3
ax.bar(x - w/2, amb, w, label='Ambulance', color='#3498db', alpha=0.8)
ax.bar(x + w/2, gh, w, label='Golden Hour', color='#f39c12', alpha=0.8)
ax.set_xticks(x)
ax.set_xticklabels(['Trauma', 'District', 'Rural'], fontsize=9)
ax.set_ylabel('Rate (%)')
ax.set_title('Prehospital: Ambulance & Golden Hour')
ax.legend(fontsize=8)
# Panel 6: ISS distribution
ax = axes[2, 1]
for sc in SCENARIOS:
if sc in dfs:
ax.hist(dfs[sc]['iss'].clip(1, 50), bins=20, alpha=0.5,
label=sc.replace('_', ' ').title()[:12], edgecolor='white')
ax.axvline(x=16, color='red', linestyle='--', alpha=0.7, label='Severe (ISS≥16)')
ax.set_xlabel('ISS')
ax.set_title('Injury Severity Score Distribution')
ax.legend(fontsize=7)
# Panel 7: Age-sex distribution
ax = axes[3, 0]
males = df[df['sex'] == 'M']['age_years']
females = df[df['sex'] == 'F']['age_years']
ax.hist(males, bins=15, alpha=0.5, color='#3498db', label='Male', edgecolor='white')
ax.hist(females, bins=15, alpha=0.5, color='#e74c3c', label='Female', edgecolor='white')
ax.set_xlabel('Age (years)')
ax.set_title('Age-Sex Distribution (Males 75%, peak 15-44y)')
ax.legend(fontsize=8)
# Panel 8: Disability at discharge
ax = axes[3, 1]
surv = df[df['outcome'] == 'survived']
if len(surv) > 0:
dis = surv['disability_at_discharge'].value_counts()
d_colors = ['#2ecc71', '#f39c12', '#e74c3c', '#9b59b6', '#3498db']
ax.pie(dis.values,
labels=[d.replace('_', ' ').title() for d in dis.index],
autopct='%1.1f%%', colors=d_colors[:len(dis)],
startangle=90, textprops={'fontsize': 8})
ax.set_title('Disability at Discharge (Survivors)')
plt.tight_layout(rect=[0, 0, 1, 0.97])
plt.savefig(output, dpi=150, bbox_inches='tight')
print(f'Saved validation report to {output}')
plt.close()
if __name__ == '__main__':
dfs = load_scenarios()
if dfs:
make_report(dfs)
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