African Sickle Cell Disease Synthetic Data for Genotype-Based Crisis Prediction

Sub-Saharan Africa, which accounts for 75% of global sickle cell disease births, is the focus of this synthetic dataset. It is designed for modeling genotype-specific severity patterns, crisis prediction, and mortality risk in resource-limited settings. The dataset was created by electricsheepafrica and released in November 2024.

Use Cases

• Predicting sickle cell crisis events based on genotype-specific severity patterns.
• Modeling mortality risk for patients in resource-limited settings.
• Training classification models for disease severity using synthetic clinical features.
• Developing regression models to estimate individual patient risk scores.

Strengths

• Dataset is explicitly designed for Sub-Saharan Africa, representing 75% of global SCD births.
• Version 1.0 was released in November 2024, indicating recent creation.
• Platform tags indicate it is structured for both multi-class classification and regression tasks.

Limitations

• Data is synthetic, which may limit real-world applicability and generalizability.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and file size are unknown, which may limit suitability assessment.

Provenance

Source

electricsheepafrica on Hugging Face.

Collection Method

Synthetically generated for modeling.

Freshness

Last updated 2025-11-07 10:29:43.

Geography

Sub-Saharan Africa.