Datasets:

electricsheepafrica
/

epilepsy-neurological

license: cc-by-4.0
task_categories:
  - tabular-classification
language:
  - en
tags:
  - healthcare
  - epilepsy
  - neurology
  - seizure
  - antiepileptic-drug
  - treatment-gap
  - sub-saharan-africa
  - lmic
pretty_name: >-
  Epilepsy & Neurological Disorders (Seizures, AED Access, Treatment Gap,
  Stigma)
size_categories:
  - 10K<n<100K
configs:
  - config_name: neurology_clinic
    data_files: data/epilepsy_neurology_clinic.csv
  - config_name: district_hospital
    data_files: data/epilepsy_district_hospital.csv
    default: true
  - config_name: rural_health_centre
    data_files: data/epilepsy_rural_health_centre.csv

Epilepsy & Neurological Disorders Dataset

Abstract

This dataset provides 30,000 simulated epilepsy patient records (10,000 per scenario) from sub-Saharan Africa. Each record contains 45+ variables including seizure type, etiology, AED access, treatment gap, stigma, psychosocial impact, and outcomes. Three settings: neurology clinic (25% treatment gap), district hospital (55%), and rural health centre (80%).

1. Introduction

Epilepsy prevalence in SSA is 4-15 per 1,000 — among the highest globally. The treatment gap exceeds 75% in rural areas. Phenobarbital is often the only AED available. There are fewer than 0.1 neurologists per 100,000 population. EEG is rarely available outside urban centres. Stigma drives 40-50% of patients to conceal their diagnosis and seek traditional/faith healers first.

This dataset is entirely simulated. It must not be used for clinical decision-making.

2. Methodology

2.1 Parameterization

Parameter	Value	Source
Prevalence SSA	4-15/1000	PMC 2024
Treatment gap rural	>75%	PMC 2012
Neurologists per 100K	<0.1	Lancet Neurology
Phenobarbital only AED	Common	PMC 2024
Stigma prevalence	40-50%	Literature

2.2 Scenario Design

Scenario	Neurologist	EEG	AEDs	Treatment Gap
Neurology clinic	Yes	Yes	Multiple	25%
District hospital	No	No	Phenobarbital	55%
Rural HC	No	No	Often stocked out	80%

3. Schema

Column	Type	Description
id	int	Unique identifier
age	int	Patient age
seizure_type	categorical	GTC / focal / absence / myoclonic
etiology	categorical	unknown / perinatal / CNS infection / TBI
in_treatment_gap	binary	Not receiving any AED
aed_prescribed	categorical	phenobarbital / carbamazepine / valproate / none
aed_adherent	binary	Taking AED as prescribed
stigma_experienced	binary	Social stigma
traditional_healer_consulted	binary	Consulted traditional healer
seizure_free_12m	binary	No seizures in 12 months

4. Validation

Validation Report

Key validation checks:

Treatment gap: 25% → 55% → 80% ✓
AED prescribed: 76% → 36% → 16% ✓
Seizure-free 12m: 15% → 8% → 3% ✓
Stigma: ~50% across scenarios ✓
Traditional healer: ~40% ✓
Stock-outs: 10% → 25% → 45% ✓

5. Usage

from datasets import load_dataset
dataset = load_dataset("electricsheepafrica/epilepsy-neurological", "district_hospital")
df = dataset["train"].to_pandas()

6. Limitations

Simulated: Not from real epilepsy registries.
No EEG data: No waveform or neuroimaging data.
Simplified: No detailed pharmacokinetics.
No longitudinal: Single time-point per patient.

7. References

PMC (2012). Epilepsy treatment SSA closing the gap.
PMC (2024). Epilepsy in Africa multifaceted perspective.
WHO (2024). Epilepsy fact sheet.
Lancet Neurology. Neurology workforce SSA.

Citation

@dataset{esa_epilepsy_neuro_2025,
  title={Epilepsy and Neurological Disorders Dataset},
  author={Electric Sheep Africa},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/electricsheepafrica/epilepsy-neurological}
}

License

CC-BY-4.0