Enterprise AI Analysis
Predicting Infant Mortality with Deep Learning in Ethiopia
Leveraging advanced Artificial Neural Networks, this analysis provides a robust predictive model for infant mortality status, identifying critical risk factors and informing targeted public health interventions in resource-scarce settings.
Executive Impact & Key Findings
Our deep learning model demonstrates superior predictive capabilities for infant mortality, offering actionable insights for healthcare policy and resource allocation.
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Prediction Accuracy
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Key Risk Factors Identified
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ML Models Compared
Deep Analysis & Enterprise Applications
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96.2%
Accuracy in Predicting Infant Mortality
The ANN model achieved an impressive 96.2% accuracy in predicting infant mortality status, demonstrating its robust performance across unseen data.
| Model | Accuracy (%) | Recall (Mortal) (%) | AUC |
|---|---|---|---|
| Artificial Neural Network (Proposed) | 96.2 | 94.0 | 0.99 |
| Gradient Boosting | 93.5 | 87.0 | 0.95 |
| Random Forest | 92.3 | 85.5 | 0.94 |
| Logistic Regression | 89.5 | 81.0 | 0.91 |
| Decision Tree | 88.7 | 80.2 | 0.89 |
Enterprise Process Flow
Data Collection (EDHS 2019)
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Feature Extraction (16 Significant Features)
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Missing Value Handling (Deletion & Imputation)
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Data Splitting (70% Train, 20% Validate, 10% Test)
Effective data preprocessing was crucial for preparing the EDHS dataset, involving careful handling of missing values and selection of key predictors, ensuring the model's reliability.
Critical Factors in Infant Mortality
The study identified several key predictors influencing infant mortality. Infant age (months) was the most influential, with younger infants having a higher risk. Maternal age and birth order also showed significant impact, with younger/older mothers and higher birth orders increasing risk. Socio-economic factors like household size and wealth index, along with place of delivery (home vs. institutional), were moderately associated with mortality risk. Maternal education, sanitation facilities, and breastfeeding status were also considered.
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Estimated Annual Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
A typical enterprise AI journey with us involves these key phases, tailored to your specific needs and infrastructure.
Phase 01: Discovery & Strategy
In-depth analysis of your current operations, data landscape, and strategic objectives. We identify key problem areas where AI can deliver maximum impact.
Phase 02: Data Preparation & Model Development
Collecting, cleaning, and structuring relevant data. Our AI specialists design, train, and validate custom models using state-of-the-art deep learning techniques.
Phase 03: Integration & Deployment
Seamless integration of the AI model into your existing systems and workflows. Rigorous testing ensures performance and stability in a live environment.
Phase 04: Monitoring & Optimization
Continuous monitoring of model performance, identifying areas for improvement, and iterative optimization to ensure sustained value and adaptability.
Phase 05: Scalability & Future Innovations
Planning for future expansion and new AI initiatives. We help you build an internal AI capability, ensuring long-term success and competitive advantage.
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