Data Science 2 Innovation Documentation

Table of Contents

1. Project Overview
2. Project Goals
3. Key Features
4. Installation
   • Prerequisites
   • Steps
5. Usage
6. License

Live Demo of Our Solution

Safe Mom: Predictive Machine Learning for Preeclampsia

Project Overview:

Safe Mom is a predictive machine learning solution aimed at helping expectant mothers and healthcare professionals identify the likelihood of developing preeclampsia during the early stages of pregnancy. By leveraging patient data collected during the eighth week, the model provides valuable insights to mitigate risks and improve maternal health outcomes.

Project Goals:

• Predict early: Identify the likelihood of preeclampsia at the eighth week of pregnancy.
• Enhance care: Empower healthcare providers to make informed decisions.
• Improve outcomes: Provide mothers with early intervention strategies for better health.

Key Features:

• Machine Learning Model: Utilizes advanced predictive algorithms to assess preeclampsia risk.
• Patient Data: Collects relevant health information.
• User Interface: Designed to be intuitive, ensuring easy input of patient data.

Installation

Prerequisites

• Python 3.x installed
• MySQL database set up
• pip for Python package installation

Steps

1. Clone this repository to your local machine.

   git clone https://github.com/SebbieMzingKe/safe-mom.git

2. Navigate into the project directory.

   cd safe-mom

3. Set up a virtual environment.

   python -m venv env
   env\Scripts\activate  # On Mac, use `source env/bin/activate`

4. Install the dependencies from the requirements.txt file.

   pip install -r requirements.txt

5. Set up your MySQL database by importing the provided schema.

   psql postgresql://username:password@host/dbname # replace username, password, host and dbname with your's

6. Run the application.

   flask run or uvicorn app:app --reload or python app.py

7. If using docker

   docker pull sebbiemzing/safe-mom-web:latest

Usage

On launching the Safe Mom, clinicians log in using their credentials. Once logged in, they can access the patient data input section.

Here, clinicians will enter patient vitals such as blood pressure, proteinuria, and fetal heart rate. Once the data is submitted, the machine learning model (powered by XGBoost) will process it.

The platform will then display the model's prediction on whether the patient is at risk of developing preeclampsia. Based on the results, the model will provide tailored recommendations, such as increasing monitoring, implementing preventive measures, or referring the patient to a specialist.

License

Copyright 2024 Safe Mom

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files, to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.