Malaria Outbreak Prediction Model

A machine learning system that predicts malaria outbreaks using environmental, healthcare, and socioeconomic factors through Random Forest classification.

Project Structure

MALARIA/
├── .scannerwork/
├── venv/
├── .coverage
├── cleaning.py
├── confusion_matrix.png
├── coverage.xml
├── dim_dates.csv
├── dim_demographics.csv
├── dim_environment.csv
├── dim_health_initiatives.csv
├── dim_healthcare.csv
├── dim_infrastructure.csv
├── dim_location.csv
├── dim_prevention.csv
├── dim_socioeconomic.csv
├── dim_weather.csv
├── fact_malaria_cases.csv
├── feature_importance.csv
├── feature_importance.png
├── loading.py
├── malaria_prediction_model.py
├── processed_data.csv
└── test_malaria_db.py

Overview

This project implements a predictive model for malaria outbreak detection using machine learning techniques. The system analyzes multiple dimensions of data including:

• Environmental factors
• Demographic information
• Healthcare accessibility
• Infrastructure data
• Prevention measures
• Socioeconomic indicators
• Weather patterns

Data Sources

The project uses a dimensional data model with the following key files:

• Fact Table: fact_malaria_cases.csv - Contains the main malaria case records
• Dimension Tables:
  • dim_dates.csv - Temporal dimensions
  • dim_demographics.csv - Population demographics
  • dim_environment.csv - Environmental factors
  • dim_health_initiatives.csv - Health programs and initiatives
  • dim_healthcare.csv - Healthcare facility information
  • dim_infrastructure.csv - Infrastructure availability
  • dim_location.csv - Geographical information
  • dim_prevention.csv - Malaria prevention measures
  • dim_socioeconomic.csv - Socioeconomic indicators
  • dim_weather.csv - Weather-related data

Key Components

Data Processing

• cleaning.py: Handles data cleaning and preprocessing
• loading.py: Manages data loading and ETL processes

Model

• malaria_prediction_model.py: Contains the Random Forest implementation
• test_malaria_db.py: Test suite for database operations

Analysis Outputs

• confusion_matrix.png: Visual representation of model performance
• feature_importance.csv: Detailed feature importance scores
• feature_importance.png: Visual representation of feature importance
• processed_data.csv: Final processed dataset used for modeling

Installation

1. Clone the repository
2. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install required dependencies:

pip install -r requirements.txt

Usage

1. Data Cleaning:

python cleaning.py

2. Load and Process Data:

python loading.py

3. Train and Run Model:

python malaria_prediction_model.py

4. Run Tests:

python -m pytest test_malaria_db.py

Model Evaluation

Model performance metrics and visualizations can be found in:

• confusion_matrix.png - For model accuracy evaluation
• feature_importance.png - For understanding feature significance

Contributing

1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Create a new Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

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Note: This project uses dimensional modeling for data organization. Make sure all dimension tables are properly loaded before running the fact table integration.