Authors: Anthony Chen, Shreya Chippagiri, Robert Logan, Pratik Shetty
pd-diag-net is a deep neural classifier which uses vocal samples to predict whether a patient has Parkinsons Disease. The model is trained using the Parkinsons Disease Handwriting Database collected by the BDALab Research Group at Brno University of Technology.
Users will need to have python2.7 installed.
The model is built using the Keras library in python; all of the dependencies
are in the requirements.txt file. You can install these in a virtual
environment by running:
python -m virtualenv .venv
source .venv/bin/activate
pip install -r requirements.txt
When you are finished working with the model you can run:
deactivate
to disable the virtual environment.
In order to obtain access to the PaHaW database, you will need to fill out a licensing agreement. For more details please see the downloads section on this website.
Once the dataset has been downloaded, extract the compressed dataset into the project folder - i.e. 'PaHaW/' should be a directory at the root level.
The dataset can then be loaded into python by adding:
import process
dataset = process.load_dataset()
to your script.
As of now, evaluation of our model is done using k-fold cross validation. As such, training and evaluation are tightly coupled.
K-Fold accuracy can be done by doing the following:
import model
model.evaluate_model()
TBD - Something about reproducing the accuracy metrics included in our paper as well as instructions for running the model on new data.