ECS171 Final Project
Parkinson’s disease is a neurodegenerative disorder that limits one’s mobility and cognitive function. Its symptoms include tremors, stiff muscles in the face and body, fatigue, and cognitive and sleep impairments. These are due to the degeneration of the basal ganglia (a group of nuclei that controls voluntary movement) and dopamine deficiency. In the early stages of Parkinson’s patients may experience voice impairment, which can worsen with time. Using voice recordings from patients with Parkinson’s aids early diagnosis, as it is one of the first signs. Utilizing voice recordings for diagnosis is non-invasive, cost-effective, and offers potential for early detection. With this project, we aim to use this data and build a machine learning model to identify specific diagnostic features, in the hopes of making a Parkinson's diagnosis more accessible and help prevent rapid disease progression.
Dataset: Shreya Dutta’s Parkinson’s Disease Dataset from Kaggle
How to run:
Open 2 terminals, one for the backend and one for the frontend.
First Terminal:
cd backend
python -m venv venv source venv/bin/activate pip install -r requirements.txt python app.py
Second Terminal:
cd frontend npm install npm run dev