Dataset Overview
The Somalia Mortality Risk Factors & Prediction Dataset is a structured, AI-ready dataset designed to support advanced analytics, machine learning, and time-series forecasting of mortality trends in Somalia. The dataset integrates key multi-sectoral risk indicators used to understand mortality patterns, vulnerability dynamics, and population-level health outcomes.
Prepared in a machine-learning-ready format, this dataset serves as a foundational input for predictive modeling, early-warning systems, and public health decision-support platforms. It is optimized for academic research, AI development, and policy-relevant analytics related to health, nutrition, climate stress, conflict exposure, and socio-economic conditions.
This dataset reflects the SIMAD AI Institute’s commitment to advancing AI for Public Health, Humanitarian Forecasting, and National Data Intelligence under the Somalia National AI Hub.
Dataset Structure
The dataset is provided in a structured tabular format (Excel / CSV compatible) and is organized for direct integration into:
- Machine learning and deep learning pipelines
- Statistical and epidemiological modeling systems
- Time-series forecasting frameworks
- Risk scoring and early-warning platforms
- Public health and humanitarian analytics tools