| --- |
| license: apache-2.0 |
| language: |
| - en |
| - ha |
| - yo |
| - fr |
| tags: |
| - healthcare |
| - nigeria |
| - malaria |
| - environmental-health |
| - africa |
| - autoscientist |
| base_model: meta-llama/Llama-3.3-70B-Instruct |
| --- |
| |
| # Nigeria Malaria 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 malaria health data (2010–2021). |
| It predicts and explains malaria prevalence risk from ITN coverage, immunization |
| and child mortality indicators across Nigeria. |
|
|
| ## Training Data |
| - Source: DHS Nigeria national surveys (2010–2021) |
| - Dataset: malaria parasitemia, ITN coverage, immunization, child mortality |
| - Quality improvement: 163.3% (Grade D → B) |
| - Kaggle: https://www.kaggle.com/datasets/yunusahusseinadeiza/nigeria-malaria-risk-model-prevalence-prediction |
|
|
| ## Training Metrics |
| - Win rate: 65% adapted vs 35% base model |
| - Base model: meta-llama/Llama-3.3-70B-Instruct |
| - Method: LoRA (r=16, alpha=32, all-linear) |
| - Epochs: 1 |
|
|
| ## Key Finding |
| Malaria prevalence rose from 36.2% in 2018 back to 39.6% in 2021 despite |
| increased ITN coverage — signaling a sustained coverage gap. |
| Pregnant women ITN coverage is the strongest protective factor against malaria. |
|
|
| ## Why This Matters |
| Nigeria carries the world's largest malaria burden. This model addresses the |
| gap in African epidemiological AI — built by a Licensed Environmental Health |
| Officer with real field experience in Abuja. |
|
|
| ## Credits |
| Powered by Adaptive Data — Adaption Labs |
| AutoScientist Challenge 2026 |
