Problem Statement
The problem we are addressing is the increasing prevalence of type 2 diabetes and the need for a more efficient and accurate way of identifying individuals who are at risk. Current diagnostic methods rely on invasive tests and can be time-consuming and costly, leading to delays in treatment and poor health outcomes. Therefore, there is a need for a predictive tool that can use demographic and lifestyle information to accurately identify individuals who are at risk of developing type 2 diabetes, allowing for early intervention and prevention measures.
Objectives for this project :
● Develop a machine learning model that can accurately predict Type2 Diabetes based on data given by the patient.
● Evaluate the performance of the model using real-world patient data.
● Deploy the Machine Learning Model to Azure so that it is easily accessible through a web domain .
● Use the deployed Model as an API to a user-friendly Web page that patients can easily access and use.
Technologies Used :
● Python for Model Training and Flask App
● ReactJs for the frontend Interface
● Azure app service for cloud Deployment
There are two part to this project ,
- Frontend Reactapp
See the App Readme for informantion
- Flaskapp -the ML API here
To run the Flask App use this commands
git clone https://github.com/WahomeKezia/FlaskApp_API
pip install gunicorn
gunicorn wsgi:app
This will run the application locally
Deployed Access:
Access the deployed ML API here
Video Walkthrough:
About Us page :
