Syded74/malaria-drug-resistance-ghana
Domaine:
healthcaregeospatialType de record:
project
Créateur:
Syd
Hôte:
AI-driven malaria drug resistance analysis for Ghana
# AI-Driven Malaria Drug Resistance Analysis for Ghana
## Table of Contents
- Project Overview
- Background and Significance
- Key Objectives
- Methodology
- Data Collection
- Data Preprocessing
- Feature Engineering
- Machine Learning Approaches
- Geospatial Analysis
- Validation Strategies
- Technical Architecture
- Datasets
- Repository Structure
- Installation and Setup
- Prerequisites
- Environment Setup
- Data Access
- Usage Guide
- Running Analysis Notebooks
- Model Training
- Generating Visualizations
- Key Findings
- Project Timeline
- Future Directions
- Contributing
- Contribution Guidelines
- Code of Conduct
- Ethics and Data Privacy
- Publication Plan
- License
- Acknowledgments
- References
- Team
- Contact
## Project Overview
This repository contains a comprehensive analytical framework for studying antimalarial drug resistance patterns in Ghana using artificial intelligence and machine learning techniques. By integrating genomic data, clinical outcomes, demographic information, and geographical data, this project aims to develop predictive models that can identify emerging resistance patterns and inform evidence-based public health interventions to combat malaria in Ghana.
Last updated: 2025-07-10
## Background and Significance
Malaria remains one of the most significant public health challenges in sub-Saharan Africa, with Ghana among the high-burden countries. Despite progress in malaria control efforts, antimalarial drug resistance poses a serious threat to treatment efficacy and disease management. The emergence and spread of resistance to artemisinin-based combination therapies (ACTs), the current first-line treatment for uncomplicated malaria, is particularly concerning.
Ghana has reported varying levels of treatment failure and delayed parasite clearance, potential indicators of emerging drug resistance. Understanding the patterns, drivers, and predictors of this resistance is crucial for:
1. Maintaining effective treatment regim …