Datasets:
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4d8a13f | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 | #!/usr/bin/env python3
"""Validation & Diagnostic Visualization for Oral Health Dataset."""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
SCENARIOS = ['dental_clinic', 'district_hospital', 'rural_health_centre']
def load_scenarios(data_dir='data'):
dfs = {}
for sc in SCENARIOS:
path = os.path.join(data_dir, f'oral_{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('Oral Health & Dental Disease — Validation Report',
fontsize=16, fontweight='bold', y=0.98)
df = dfs.get('district_hospital', list(dfs.values())[0])
colors = ['#2ecc71', '#f39c12', '#e74c3c']
ax = axes[0, 0]
conditions = ['dental_caries', 'untreated_caries', 'periodontal_disease',
'dental_pain', 'dental_abscess', 'tooth_loss']
c_labels = ['Caries', 'Untreated', 'Periodontal', 'Pain', 'Abscess', 'Tooth Loss']
vals = [df[c].mean() * 100 if c != 'tooth_loss' else (df[c] > 0).mean() * 100 for c in conditions]
ax.bar(range(6), vals, color='#e74c3c', alpha=0.7)
ax.set_xticks(range(6))
ax.set_xticklabels(c_labels, fontsize=8, rotation=15)
for i, v in enumerate(vals):
ax.text(i, v + 0.5, f'{v:.0f}%', ha='center', fontsize=8)
ax.set_ylabel('Prevalence (%)')
ax.set_title('Oral Disease Burden (caries most prevalent)')
ax = axes[0, 1]
x = np.arange(len(SCENARIOS))
care = [dfs[sc]['sought_dental_care'].mean() * 100 for sc in SCENARIOS if sc in dfs]
ax.bar(x, care, color=colors, alpha=0.8)
ax.set_xticks(x)
ax.set_xticklabels(['Dental Clinic', 'District', 'Rural'], fontsize=9)
for i, v in enumerate(care):
ax.text(i, v + 0.5, f'{v:.0f}%', ha='center', fontsize=10)
ax.set_ylabel('Care-Seeking Rate (%)')
ax.set_title('Dental Care Access (<1 dentist/100K in SSA)')
ax = axes[1, 0]
caries_pts = df[df['dental_caries'] == 1]
if len(caries_pts) > 0:
ax.hist(caries_pts['dmft_score'], bins=20, color='#3498db', alpha=0.7, edgecolor='white')
ax.set_xlabel('DMFT Score')
ax.set_title('DMFT Distribution (mean ~4 in SSA)')
ax = axes[1, 1]
treatments = df[df['treatment_received'] != 'none']['treatment_received'].value_counts()
if len(treatments) > 0:
t_colors = ['#e74c3c', '#f39c12', '#3498db', '#2ecc71', '#9b59b6', '#e67e22']
ax.pie(treatments.values,
labels=[s.replace('_', ' ').title() for s in treatments.index],
autopct='%1.0f%%', colors=t_colors[:len(treatments)],
startangle=90, textprops={'fontsize': 8})
ax.set_title('Treatment Type (extraction dominates)')
ax = axes[2, 0]
barriers = df[df['barrier_to_care'] != 'none']['barrier_to_care'].value_counts()
if len(barriers) > 0:
ax.barh(range(len(barriers)), barriers.values, color='#3498db', alpha=0.8)
ax.set_yticks(range(len(barriers)))
ax.set_yticklabels([s.replace('_', ' ').title() for s in barriers.index], fontsize=8)
ax.set_xlabel('Count')
ax.set_title('Barriers to Dental Care')
ax = axes[2, 1]
risks = ['sugary_diet', 'tobacco_use', 'fluoride_toothpaste', 'diabetes']
r_labels = ['Sugary Diet', 'Tobacco', 'Fluoride Paste', 'Diabetes']
caries_y = df[df['dental_caries'] == 1]
caries_n = df[df['dental_caries'] == 0]
if len(caries_y) > 0 and len(caries_n) > 0:
vc = [caries_y[r].mean() * 100 for r in risks]
vn = [caries_n[r].mean() * 100 for r in risks]
w = 0.3
ax.bar(np.arange(4) - w/2, vc, w, label='Caries', color='#e74c3c', alpha=0.8)
ax.bar(np.arange(4) + w/2, vn, w, label='No Caries', color='#2ecc71', alpha=0.8)
ax.set_xticks(np.arange(4))
ax.set_xticklabels(r_labels, fontsize=8)
ax.set_ylabel('Prevalence (%)')
ax.set_title('Risk Factors vs Caries')
ax.legend(fontsize=8)
ax = axes[3, 0]
children = df[df['child'] == 1]
adults = df[df['child'] == 0]
cats = ['Caries', 'Pain', 'Sought Care']
if len(children) > 0 and len(adults) > 0:
vc = [children['dental_caries'].mean()*100, children['dental_pain'].mean()*100,
children['sought_dental_care'].mean()*100]
va = [adults['dental_caries'].mean()*100, adults['dental_pain'].mean()*100,
adults['sought_dental_care'].mean()*100]
w = 0.3
ax.bar(np.arange(3) - w/2, vc, w, label='Children', color='#3498db', alpha=0.8)
ax.bar(np.arange(3) + w/2, va, w, label='Adults', color='#e74c3c', alpha=0.8)
ax.set_xticks(np.arange(3))
ax.set_xticklabels(cats, fontsize=9)
ax.set_ylabel('Rate (%)')
ax.set_title('Children vs Adults')
ax.legend(fontsize=8)
ax = axes[3, 1]
brush = df['brushing_frequency'].value_counts()
b_order = ['never', 'occasional', 'once_daily', 'twice_daily']
vals = [brush.get(b, 0) for b in b_order]
ax.bar(range(4), vals, color=['#e74c3c', '#f39c12', '#3498db', '#2ecc71'], alpha=0.8)
ax.set_xticks(range(4))
ax.set_xticklabels(['Never', 'Occasional', 'Once/Day', 'Twice/Day'], fontsize=8)
ax.set_ylabel('Count')
ax.set_title('Brushing Frequency')
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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