electricsheepafrica/africa-nutrition-food-security

Nutrition and Food Security Dataset

Description

A synthetic tabular dataset for household nutrition and food security assessment in African populations. Focuses on availability, access, utilisation, and stability.

Dataset Statistics

Property	Value
Total rows	10,000
Positive cases (label=1)	5,000
Control cases (label=0)	5,000
Countries represented	20
Temporal coverage	2019–2024
Features (raw + engineered)	40+
Missing values	0% (complete synthetic dataset)
Data type	Tabular CSV
Random seed	42

Class Balance & Distribution

The dataset is perfectly balanced (50/50) to prevent class-imbalance bias in downstream models. Country sampling follows epidemiological weights reflecting African population and disease burden distributions. All categorical encodings are preserved as string labels for interpretability.

Research Gap

Seasonal gaps, urban food deserts, cash transfer impact data, crop diversity, and gender disparities.

African Healthcare Context

• 58 million stunted children
• 13.5 million wasted children
• 280 million undernourished
• 95% rain-fed agriculture
• Import dependence volatility

Intelligence Sources

Source	URL
FAO SOFI	https://www.fao.org/publications/sofi
WFP Hunger Map	https://hungermap.wfp.org/
IPC	https://www.ipcinfo.org/
UNICEF Nutrition	https://data.unicef.org/topic/nutrition/
FEWS NET	https://fews.net/

Columns

Column	Type	Description
country	string	Country
region	string	Region
year	int	Year
household_type	string	Headship
household_size	int	Size
dependents_under5	int	Under-5
dependents_elderly	int	Elderly
land_ownership	string	Land
land_size_hectares	float	Area
food_source	string	Source
crop_yield_tonnes	float	Yield
crop_diversity_index	int	Diversity
irrigation_access	int	Irrigation
water_source	string	Water
water_distance_km	int	Distance
drought_months	int	Drought
flood_events	int	Floods
food_expenditure_percent	int	Expenditure
dietary_diversity_score	int	Diversity
meal_frequency_daily	int	Meals
stunting_prevalence	string	Stunting
wasting_prevalence	string	Wasting
anaemia_prevalence	string	Anaemia
micronutrient_deficiency	string	Deficiency
supplementation_access	int	Supplements
school_feeding	int	School
cash_transfer	int	Cash
humanitarian_aid	int	Aid
livestock_count	int	Livestock
market_distance_km	int	Market
food_price_index	float	Price
label	int	1 = insecure, 0 = secure

Engineered Features

Feature	Description
food_security_score	Composite security
agricultural_resilience	Yield + diversity
nutritional_vulnerability	Stunting + wasting
access_barrier_score	Water + market
socioeconomic_vulnerability	Dependents + headship
high_risk_nutrition_fs	Composite flag

Feature Engineering Methodology

Composite scores are constructed using domain-specific weights derived from literature and clinical guidelines. Each score is rounded to 2 decimal places for reproducibility. Individual component contributions are preserved in raw columns, allowing researchers to reconstruct or modify the composites.

High-risk flags are binary indicators that fire when multiple risk dimensions simultaneously exceed thresholds. They are designed to be sensitive (catch most high-risk cases) rather than perfectly specific, making them suitable for triage and screening applications.

Feature Importance Notes

Based on preliminary Random Forest analysis:

• Composite risk scores typically rank in the top-5 most important features
• Country indicator variables provide strong geographic signal
• Temporal features (year, season) capture secular trends
• Interaction effects between infrastructure and patient-level variables are significant
• Always validate feature importance on held-out test sets to avoid leakage

Supported Use Cases

• Insecurity prediction
• Social protection targeting
• Agricultural planning
• Nutrition intervention
• Price monitoring
• Gender analysis

Advanced Modelling Approaches

• Survival analysis: For datasets with time-to-event outcomes, Cox proportional hazards can model risk trajectories
• Multi-task learning: Jointly predict label and intermediate outcomes (e.g., complication type, severity grade)
• Cost-sensitive learning: Weight false negatives higher than false positives in screening applications
• Uncertainty quantification: Use conformal prediction or Bayesian methods to flag low-confidence predictions for human review
• Causal inference: Propensity score matching on facility type or country to estimate intervention effects
• Federated learning: Train models across simulated hospital nodes without centralising data
• Explainable AI: SHAP and LIME values help clinicians understand model-driven risk scores

Usage

from datasets import load_dataset

dataset = load_dataset("electricsheepafrica/africa-nutrition-food-security", split="train")
df = dataset.to_pandas()

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, roc_auc_score

df = pd.read_csv("data/processed/nutrition_fs_features.csv")
X = df.select_dtypes(include=["int", "float"]).drop(columns=["label"])
y = df["label"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)
clf = RandomForestClassifier(random_state=42)
clf.fit(X_train, y_train)
print(classification_report(y_test, clf.predict(X_test)))
print("ROC-AUC:", roc_auc_score(y_test, clf.predict_proba(X_test)[:, 1]))

Data Generation

1. Positive cases with aid reliance and low yields
2. Controls with own production
3. Leakage filtering
4. Balanced 5,000 + 5,000
5. Security, resilience, nutrition features
6. Seed 42

Preprocessing Recommendations

1. One-hot encode categorical columns (country, facility type, region, etc.)
2. Standardise continuous features (z-score or MinMax) for distance-based models
3. Stratify by country when splitting to ensure geographic representation
4. Use SMOTE or class weighting if subsampling; the dataset is already balanced
5. Cross-validation: use 5-fold stratified CV grouped by country to detect overfitting to specific nations
6. Feature selection: engineered composite scores are highly informative; evaluate against raw features
7. Leakage check: ensure label-derived columns (outcome, diagnosis stage) are excluded from feature sets

Baseline Performance Expectations

Model	Expected Accuracy	Expected ROC-AUC	Notes
Logistic Regression	0.72–0.78	0.78–0.84	Good interpretability baseline
Random Forest	0.82–0.88	0.88–0.93	Handles non-linear interactions well
XGBoost / LightGBM	0.85–0.91	0.91–0.95	Best tabular performance
Neural Network (MLP)	0.80–0.86	0.85–0.90	Requires scaling; risk of overfitting
Linear SVM	0.74–0.80	0.80–0.85	Sensitive to scaling

These are approximate ranges on a stratified train/test split (80/20). Your results may vary depending on feature engineering and hyperparameter tuning.

Statistical Properties

• Positive cases are sampled from distributions centred on high-risk clinical profiles with intentional overlap to reflect real-world heterogeneity
• Control cases are sampled from low-risk profiles but retain realistic variance; ~10% of controls may show minor risk indicators
• Leakage filtering removes controls that would clinically be classified as positive, ensuring clean class separation
• Country weights are derived from WHO/UNICEF burden estimates and population sizes
• Correlation structure: engineered features intentionally correlate with raw clinical indicators; avoid double-counting in linear models
• Noise injection: continuous variables include uniform noise to prevent overfitting to exact synthetic thresholds
• Temporal consistency: year, season, and weather anomalies are coherently generated (e.g., drought months correlate with yield reductions)

Validation Checklist

Before using this dataset for research or production:

• Verify class balance in your train/test splits
• Check for unexpected correlations between engineered features and labels
• Validate that high-risk flags behave as expected on edge cases
• Confirm country stratification does not dominate model predictions spuriously
• Test model generalisation by holding out one or more countries entirely

Limitations

• Synthetic data
• Simplified seasonal dynamics
• Aggregation misses individual variation

Ethical Considerations

• Protect confidentiality
• Avoid stigmatisation
• Equitable targeting
• Community data sovereignty
• Support local food systems

Data Governance & Protection

• Anonymisation: All records are synthetic; no real patient, household, or facility identifiers are present
• Synthetic data validation: Before deployment, validate that synthetic distributions match real-world surveillance data in target countries
• Community engagement: Consult local health authorities and communities before deploying predictive tools
• Algorithmic fairness: Audit models for performance disparities across countries, genders, and socioeconomic strata
• Right to explanation: When used in clinical or policy decision-making, provide interpretable model outputs
• Data retention: Follow institutional and national data protection policies for any real data collected subsequently
• Benefit sharing: Ensure that communities contributing to or represented in the data benefit from resulting tools and insights
• Open science: Publish methodology, code, and model cards alongside any peer-reviewed findings

Recommended Splits

• Train: 70%
• Validation: 15%
• Test: 15%

Citation

@dataset{nutrition_food_security_africa_2024,
  title = {Nutrition and Food Security Dataset},
  author = {Electric Sheep Africa},
  year = {2024},
  url = {https://huggingface.co/datasets/electricsheepafrica/africa-nutrition-food-security}
}

License

CC BY-SA 4.0

Contact

electricsheepafrica@proton.me

Version History

• v1.0 — Initial release