pyParkinsons

pyParkinsons is a project used for finding early detections of Parkinsons disease using machine learning librairies pandas and XGBoost

Requirements

This repository has included a requirements.txt file for all the required dependencies. A virtual environment is also reccomended for all python projects, but it is optional.

Virtual python environment installation using pip

pip install virtualenv

Dependency librairies installation using pip

pip install numpy pandas sklearn xgboost

Usage

This program can be used with a different .data file, as the data filed included is just an example. I used the UCI Parkinsons Data Set to train the algorithim. To have the program work correctly, the .data file needs to be structured in columns with

• name - ASCII subject name and recording number
• MDVP:Fo(Hz) - Average vocal fundamental frequency
• MDVP:Fhi(Hz) - Maximum vocal fundamental frequency
• MDVP:Flo(Hz) - Minimum vocal fundamental frequency
• MDVP:Jitter(%),MDVP:Jitter(Abs),MDVP:RAP,MDVP:PPQ,Jitter:DDP - Several measures of variation in fundamental frequency
• MDVP:Shimmer,MDVP:Shimmer(dB),Shimmer:APQ3,Shimmer:APQ5,MDVP:APQ,* Shimmer:DDA - Several measures of variation in amplitude
• NHR,HNR - Two measures of ratio of noise to tonal components in the voice
• status - Health status of the subject (one) - Parkinson's, (zero) - healthy
• RPDE,D2 - Two nonlinear dynamical complexity measures
• DFA - Signal fractal scaling exponent
• spread1,spread2,PPE - Three nonlinear measures of fundamental frequency variation

Contributions

Pull requests are welcome, major changes should be taken up with an issue to first discuss what you would like to change

License

MIT