Eye Disease Risk Assessment

Welcome to the Eye Disease Risk Assessment repository! This repository contains code for developing a machine learning algorithm aimed at accurately assessing the risk of eye diseases in patients during routine screenings. Additionally, a portal has been developed to integrate into existing healthcare systems, facilitating early detection of eye diseases, particularly in regions with limited access to trained eye care personnel.

Objective

The primary objectives of this project are:

1. Develop a robust machine learning algorithm capable of accurately assessing the risk of eye diseases in patients during routine screenings.
2. Develop a portal to integrate into existing healthcare systems to facilitate early detection of eye diseases.

Features

• Machine Learning Algorithm: The repository contains code for developing and fine-tuning the machine learning algorithm using various techniques such as data preprocessing, feature selection, model training, and evaluation.
• Portal Development: A web-based portal has been created to integrate the developed algorithm into existing healthcare systems. The portal provides an intuitive interface for users to input patient data and receive risk assessment results.

Getting Started

To get started with this repository, follow these steps:

1. Clone the Repository: Clone this repository to your local machine using the following command:

   git clone https://github.com/your-username/eye-disease-risk-assessment.git
   

2. Set Up Environment: Set up your development environment by installing the necessary dependencies. Detailed instructions can be found in the repository's documentation.

3. Explore Code: Explore the codebase to understand the implementation details of the machine learning algorithm and the development of the portal.

4. Run the Portal: Start the portal locally to interact with the machine learning algorithm and test its functionality.

Directory Structure

The repository is organized into the following directory structure:

eye-disease-risk-assessment/
│
├── model/
│   ├── data_preprocessing.ipynb
│
├── med_portal/
│   ├── app.py
│   ├── ...
│
└── README.md

• machine_learning: Contains code related to the development and evaluation of the machine learning algorithm.
• portal: Contains code for the development of the web-based portal, including the Flask application and associated templates and static files.

Contributors

• Fiona Alulu