Over half of Chadian children under five are malnourished. This project builds a machine learning pipeline to predict which children are at highest risk — using 9,826 children from the DHS Chad 2014 survey — enabling NGOs and health workers to intervene before malnutrition becomes acute or fatal.
Enter a child's age, weight, and height to get an instant malnutrition risk assessment — designed for community health workers and NGO field teams in Chad.
Chad has one of the highest child malnutrition rates in the world:
- 42.9% of children under five are stunted
- 32.5% are underweight
- 14.2% are wasted — above the WHO emergency threshold
- 52.9% suffer from some form of malnutrition
Yet most humanitarian responses are reactive — children are identified only when they arrive at a health facility in crisis. This project asks: can we predict which children are at highest risk before they become acutely malnourished?
- Source: DHS Program — Chad Standard DHS 2014
- File: Children's Recode (KR) — 18,623 total children
- Measured: 9,826 children with anthropometric measurements
- Target: Any malnutrition (stunting OR underweight OR wasting)
| Indicator | Column | Threshold | Prevalence |
|---|---|---|---|
| Stunting | HW70 (HAZ) | Z-score < -2 | 42.9% |
| Underweight | HW71 (WAZ) | Z-score < -2 | 32.5% |
| Wasting | HW72 (WHZ) | Z-score < -2 | 14.2% |
| Any malnutrition | Combined | Any above | 52.9% |
DHS Chad 2014 (18,623 children)
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Filter to measured children (9,826)
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Extract malnutrition Z-scores (HW70, HW71, HW72)
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Feature selection — 28 variables across 5 domains
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Remove redundant features (correlation analysis)
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Median imputation for missing values
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7 ML classifiers benchmarked
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5-Fold Stratified Cross-Validation
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Feature importance analysis
| Domain | Features |
|---|---|
| Child anthropometrics | Age in months, Weight, Height |
| Mother health | BMI, Height, Education, Age |
| Birth history | Birth order, Preceding birth interval, Births in 5 years |
| Household | Wealth index, Size, Water source, Toilet, Electricity |
| Feeding practices | Breastfeeding duration, Weaning age |
| Model | Accuracy | ROC-AUC |
|---|---|---|
| Gradient Boosting | 92.0% ✅ | 0.979 ✅ |
| XGBoost | 91.8% | 0.979 |
| CatBoost | 91.3% | 0.974 |
| Decision Tree | 85.1% | 0.919 |
| Logistic Regression | 80.4% | 0.886 |
| Random Forest | 79.2% | 0.889 |
| KNN | 67.2% | 0.732 |
All models evaluated using 5-Fold Stratified Cross-Validation on 9,826 children.
52.9% of children under five in Chad suffer from stunting, underweight, or wasting — making malnutrition the default condition, not the exception.
Child age, weight, and height alone account for ~95% of the model's predictive power. A community health worker with a scale and measuring tape can screen children effectively.
Child age is the strongest predictor (importance: 0.421). Stunting accumulates over time — early intervention before age 2 is critical to prevent irreversible damage.
Mother's height predicts child stunting — malnourished mothers raise malnourished children. Breaking this cycle requires maternal nutrition programs, not just child feeding.
Water source, toilet type, and electricity show near-zero predictive importance — suggesting that in Chad's context, maternal and child anthropometrics are far stronger signals than household infrastructure.
| Rank | Feature | Description | Importance |
|---|---|---|---|
| 1 | HW1 | Child age in months | 0.421 |
| 2 | HW2 | Child weight kg | 0.301 |
| 3 | HW3 | Child height cm | 0.229 |
| 4 | V438 | Mother height cm | 0.007 |
| 5 | V445 | Mother BMI | 0.006 |
| 6 | V191 | Wealth index score | 0.005 |
| 7 | V221 | Marriage to first birth interval | 0.004 |
| 8 | V133 | Mother education years | 0.004 |
| 9 | M7 | Duration of breastfeeding | 0.003 |
| 10 | B11 | Preceding birth interval | 0.003 |
# Clone
git clone https://github.com/Derio001/chad-malnutrition-prediction
cd chad-malnutrition-prediction
# Install dependencies
pip install pandas numpy matplotlib seaborn scikit-learn
pip install xgboost catboost
# Run notebook
jupyter notebook Chad_Malnutrition_Prediction.ipynbNote on data: DHS data requires free registration at dhsprogram.com. Request Chad 2014 KR recode dataset. The processed modeling dataset is included in this repo.
chad-malnutrition-prediction/
│
├── Chad_Malnutrition_Prediction.ipynb # Full pipeline
├── chad_malnutrition_model_data.csv # Processed dataset
├── malnutrition_model_comparison.csv # Model results
├── malnutrition_feature_importance.csv # Feature rankings
└── README.md
- Regional disaggregation — Sahel vs southern Chad
- Integrate with crop yield early warning system
- Add 2023 DHS data when available for Chad
- Build Streamlit dashboard for field worker use
- Extend to severe acute malnutrition (SAM) prediction
- Multi-country model (Niger, Mali, Sudan)
| Organization | Application |
|---|---|
| UNICEF Chad | Target child nutrition programs |
| WFP Chad | Link food security to malnutrition risk |
| MSF / Action Against Hunger | Field screening tool |
| Ministry of Health Chad | National nutrition surveillance |
| World Bank | Evidence base for nutrition investment |
Mahamat Hanga Derio M.Tech Data Science — Christ University, Bangalore Chadian national | Building ML tools for public health and development in Sub-Saharan Africa
📬 Open to collaboration with UNICEF, WFP, WHO, NGOs and research institutions working on child health in Chad 🔗 GitHub | LRI Project | Crop Early Warning | GDP Dashboard
Part of an integrated data science portfolio focused on child welfare, food security, and economic development in Chad and the Lake Chad Basin.
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Now create the GitHub repo `chad-malnutrition-prediction`, upload the files, paste this README, and add topics:
machine-learning chad malnutrition child-health dhs-data africa unicef public-health python gradient-boosting xgboost