We were inspired to give Doctors the ability to monitor and predict their patients risk for Diabetes, Stroke, and Heart Disease based on patient data. We wanted an all-in-one solution to help busy Doctors view patient health trends over time and offer their recommendations to the patient to improve their health.
RiskAlert is an all-in-one solution to combine assessing patient health trends over time and offering recommendations to improve patient health together in a user-friendly and visually engaging interface for Doctors.
It allows Doctors to search for their patient’s name, date of birth, or ID to locate their patient. The Doctor will then enter patient data and KNN algorithms will be used to help predict risk.
RiskAlert provides a user-friendly and visually engaging interface, the HMI Display, for Doctors to interact with the system. Our HMI Display is a gateway for communication with the system to monitor their patients and predict their risk for Diabetes, Stroke, and Heart Disease. After the risk assessment, each disease is displayed on the HMI Display and the Doctor can then write recommendations for their patients regarding each disease
Ensure accountability through communication between Doctor and their patient.
Prevent risk of disease including Diabetes, Stroke, and Heart Disease.
Provide recommendations to the patient to improve their health.
Provide peace of mind for Doctors and their patients.
We used Python for the back end. Please see the Getting Started section for further instructions, dependency management tools, and libraries needed.
We used Python and Node.js for the front end. Please see the Getting Started section for further instructions, dependency management tools, and libraries needed.
Adobe XD was used to build the prototype. We used 10 Usability Heuristics for User Interface Design to be sure the prototype was designed for as many users as possible.
From NNG.
We also used Git, GitHub, MSTeams, and Visual Studio Code.
We are proud we worked hard together over this semester. We are proud to be part of this opportunity to experiment with new technologies and showcase our project.
This was our first time designing a HMI-style project together!
In the beginning, brainstorming what our project should do was a challenge. We spent a lot of time in meetings together to understand what we were looking for as a final project.
We had some technical issues establishing communication between our C++ modules and a PyQT GUI which is a UI Module, written in Python. We unfortunately had to convert all of our code to Python, despite all of the suggestions and advice received from around the world in the programming community.
We learned that memos and note-taking always improve communication. Having notes also kept us on track. This was true from the initial brainstorming to the final submission. We will keep all memos and notes for all projects in the future. We improved our note taking, questioning skills, and documentation to record our actions and improve our communication. We also learned that during times of frustration, solutions always appear after team discussions.
We are hoping that we can make RiskAlert a reality. We hope to prevent diseases like Diabetes, Stroke, and Heart Disease and improve communication between Doctors and their patients.
Please note: after installing each library, check version
Prerequisites:
Nodejs Library:
https://nodejs.org/en/download/current
All other dependencies:
cd frontend
npm install
Running the localhost server for the frontend:
npm run dev
cd backend
#1: Setting up Python virtual environment
python -m venv venv
#2: Activate the environment
cd venv/Scripts
./activate
#4: Install pip (on Windows)
curl https://bootst/rap.pypa.io/get-pip.py -o get-pip.py
python get-pip.py
#3: Install libraries dependencies
Scikit learn library:
pip install -U scikit-learn
Numpy Library:
pip install numpy
Pandas Libray:
pip install pandas
Flask Library:
pip install Flask
Flask Cors Library:
pip install Flask-Cors
Running the localhost server for the backend:
-confirm that you are in the root of the backend folder
pwd
Your relative path should be: backend\api.py
Once confirmed, you can now run the backend server
set FLASK_APP=api.py
set FLASK_ENV=development
flask run