Explainable four-agent pipeline for cervical cancer screening prioritisation and referral navigation
---
title: CancerPath-Africaπ₯
colorFrom: blue
colorTo: red
sdk: gradio
sdk_version: 6.14.0
app_file: app.py
pinned: true
license: mit
---
# π₯ CancerPath-Africa
**An Explainable Multi-Agent AI Pipeline for Cervical Cancer Screening
Outreach Prioritisation and Referral Navigation in Kenya**
*AI in Healthcare Bootcamp 2026 β Capstone Project*
---
## π Table of Contents
- Overview
- Clinical Problem
- System Architecture
- Agent Descriptions
- Explainability Framework
- Datasets
- Key Findings
- Repository Structure
- Installation
- Usage
- Technical Stack
- Limitations
- Citation
---
## Overview
CancerPath-Africa is a modular multi-agent orchestrated AI pipeline
designed to support community health workers in Kenya with cervical
cancer screening outreach prioritisation and referral navigation.
The system integrates classical machine learning, LLM-powered
reasoning, geospatial facility mapping, and explainable AI into a
single end-to-end pipeline that is deployable in low-resource Sub-Saharan
African healthcare settings.
---
## Clinical Problem
Cervical cancer is the most prevalent cancer among Kenyan women yet
is almost entirely preventable with early screening. Three critical
gaps drive late-stage presentation:
| Gap | Statistic |
|-----|-----------|
| Screening non-uptake | 86% of Kenyan women never screened (DHS 2022) |
| Referral non-completion | 72.8% face significant distance barriers |
| Oncologist shortage | < 1 specialist per million people in SSA |
CancerPath-Africa targets all three gaps through AI-assisted
community health worker support.
---
## System Architecture
```
Patient / Health Worker Input
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β ORCHESTRATOR (orchestrator.py) β
β Central pipeline controller β
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β
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β AGENT 1 β Screening Predictor β
β Logistic Regression + SHAP β
β Threshold: 0.30 β
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