Nigeria WASH Health Model
AutoScientist Challenge 2026 | Healthcare Category
Author: Hussein Adeiza (mabera)
Role: Licensed Environmental Health Officer, Abuja Nigeria
Base Model: Llama 3.3 70B
Fine-tuned with: AutoScientist by Adaption Labs
Model Description
This is a LoRA adapter fine-tuned on Nigeria DHS WASH health data (2003–2024). It predicts and explains child diarrhea risk from water, sanitation and hygiene indicators across Nigeria.
Training Data
- Source: DHS Nigeria national surveys (2003–2024)
- Dataset: 3,972 adapted rows (Hausa, Yoruba, French, English)
- Quality improvement: 310% (Grade E → B)
- Kaggle: https://www.kaggle.com/datasets/yunusahusseinadeiza/nigeria-wash-risk-model-diarrhea-prediction
Training Metrics
- Win rate: ~70% adapted vs ~30% base model
- Base model: meta-llama/Llama-3.3-70B-Instruct
- Method: LoRA (r=16, alpha=32, all-linear)
- Epochs: 1
Why This Matters
Most health AI is trained on Western clinical data. This model addresses the gap in African environmental health AI — built specifically for Nigerian and West African public health realities.
Credits
Powered by Adaptive Data — Adaption Labs
AutoScientist Challenge 2026
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Model tree for mabera/nigeria-wash-health-model
Base model
meta-llama/Llama-3.1-70B Finetuned
meta-llama/Llama-3.3-70B-Instruct