Parkinson's Disease Progression

CSC522 - Automated Learning Data Analysis Project

Team members:

1. Srujana Rachakonda
2. Rajshree Jain
3. Sreeraksha Mavinhally Sreekantha
4. Devi Krishnan

Objective: The project aims to

1. predict Parkinson's disease progression in patients using algorithms such as support vector regression and multilayer perceptron, and
2. determine the optimal threshold value for motor UPDRS above which speech disabilities are most likely to start appearing. This could potentially help to detect early stage Parkinson's disease.

Required libraries: We used Python 3. The following packages are required before execution of the code: Pandas, Numpy, Sklearn, Tensorflow, Keras, Matplotlib. These can be installed using "sudo pip install package".

How to execute the code:

1. In order to (a)run the SVR model for regression and (b)find the optimal threshold for motor UPDRS: Type "python3 main.py" into the command line (make sure you are in the directory containing the code).

2. To run the MLP model for regression: Type "python3 mlp.py" into the command line.

3. To run the multivariate linear regression model: Type "python3 multiple_linear_regression.py" into the command line.