Enterprise AI Analysis
Machine Learning for Global Public Health: Predicting Sanitation Access in Sub-Saharan Africa
This analysis leverages machine learning to predict household sanitation facility access across 34 Sub-Saharan African countries, identifying critical socioeconomic and infrastructural determinants for targeted interventions and achieving Sustainable Development Goal 6.2.
Executive Impact & Key Metrics
Understand the critical challenges and the predictive power of AI in addressing sanitation disparities at scale.
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Unimproved Sanitation Access
0
Random Forest Accuracy
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Households Analyzed (DHS)
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Top Predictor: Shared Toilet Importance
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
49.85%
Households with Unimproved Sanitation Facility Access in SSA
This prevalence highlights a significant public health challenge in the region, influencing policy development and intervention targeting.
80.61%
Random Forest Model Accuracy
The Random Forest model demonstrated the highest predictive performance, establishing its utility for complex, multi-factor analyses in public health.
0.8377
Random Forest Model F1-Score
A high F1-score indicates a well-balanced performance, effectively identifying both improved and unimproved sanitation access cases.
Most Influential Predictors of Sanitation Access
SHAP analysis identified shared toilet (feature importance=0.233), education level (0.204), and wealth index (0.094) as the most influential predictors. Other significant factors included residence, electricity access, age of household head, drinking water source, household water treatment, and cooking fuel type. Media access, sex of household head, handwashing facility, water access, soap presence, location of water source, marital status, and household size also contributed notably. The number of under-five children had minimal predictive influence.Machine Learning Analysis Workflow
Data Collection (34 SSA DHS Countries)
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Data Preprocessing (Cleaning, Transformation, Balancing)
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Data Splitting (80% Training, 20% Testing)
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Model Training & Selection (RF, DT, XGB, LR, ANN)
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Model Evaluation (Accuracy, Precision, Recall, F1-score, AUC-ROC)
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Final Outcomes (Sanitation Facility Prediction & SHAP Feature Importance)
This workflow outlines the systematic application of machine learning for robust prediction and interpretability.
| Model | Accuracy (%) | F1-Score | AUC |
|---|---|---|---|
| Random Forest | 80.61 | 0.8377 | 0.8560 |
| Decision Tree | 79.71 | 0.8242 | 0.8345 |
| XGBoost | 77.34 | 0.8145 | 0.8305 |
| Logistic Regression | 76.91 | 0.7851 | 0.8061 |
| Artificial Neural Network | 77.26 | 0.8079 | 0.8270 |
Class Imbalance Handling with SMOTE
To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied, resulting in a balanced dataset with 200,893 instances for each class. This significantly improved the model's ability to learn from both groups and reduced bias, yielding slightly higher F1-scores and AUC values, particularly for Random Forest and Logistic Regression models.Strategic Interventions for Improved Sanitation
The findings underscore the need for targeted interventions focusing on economic empowerment, health education (especially through formal schooling and mass media campaigns), and infrastructure investment (expanding piped water systems and subsidizing latrine construction). Prioritizing households with shared toilets, low education, and limited electricity access is crucial to bridge urban-rural disparities and accelerate SDG 6.2 progress.Calculate Your Potential AI-Driven ROI
Estimate the impact of predictive analytics on your organization's efficiency and cost savings.
Annual Cost Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
A phased approach to integrating advanced analytics for public health initiatives.
Phase 01: Data Integration & Preprocessing
Consolidate diverse DHS datasets and other relevant public health data sources. Implement robust cleaning, transformation, and class balancing techniques (e.g., SMOTE) to ensure high-quality data for model training.
Phase 02: Model Development & Validation
Develop and train various machine learning models (e.g., Random Forest, XGBoost) using cross-validation. Optimize hyperparameters to achieve the highest predictive accuracy and F1-scores for sanitation facility access.
Phase 03: Feature Importance & Insight Generation
Apply SHAP analysis to identify and quantify the most influential factors driving sanitation access disparities. Translate model explanations into actionable insights for policymakers and public health programs.
Phase 04: Intervention Design & Pilot Programs
Utilize data-driven insights to design targeted economic empowerment, health education, and infrastructure investment programs. Pilot these interventions in high-risk areas identified by the model.
Phase 05: Monitoring, Evaluation & Scaling
Establish a framework for continuous monitoring of intervention effectiveness using AI-driven metrics. Evaluate progress against SDG 6.2 targets and scale successful programs across broader regions in Sub-Saharan Africa.
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