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ParkinsonVoiceSVM

"Parkinson's Disease Detection using Machine Learning - Python" Project of classification of Parkinsons's disease with dataset of voice features. Supervised classification is done by using Support Vector Machine model from Sci-kit learn library.

Content

  1. Files
  2. Dataset and video tutorial
  3. How to reference
  4. Information about the project
  5. Code Organization

1-Files

Code file is "ParkinsonVoiceSVM.py" and dataset file is "parkinsons.csv" you should download these two. There is also a model file called "svm_parkinson.joblib" to import the model in a system.

2-Dataset and video tutorial

Utilized by this video tutorial : Siddardhan Codes in tutorial are changed & re-designed based on my own decisions and practices.

3-How to reference

Please refer with names or Github links which are Siddardhan S (tutorial owner), and Oğuzhan Memiş (this repository), and the source of the dataset. Contacts are welcomed.

Dataset Oxford Parkinson's Disease Detection Dataset
2008-06-26 Cite: Max A. Little, Patrick E. McSharry, Eric J. Hunter, Lorraine O. Ramig (2008),'Suitability of dysphonia measurements for telemonitoring of Parkinson's disease',IEEE Transactions on Biomedical Engineering

4-Information about the project

Parkinson's is a neurological and progressive disease, which occurs from lack of some neurotransmitters in the brain such as Dopamine. It harms the fine-tuned control of the movements of the body. As it progress, several symptoms such as tremor, body stiffness and loss of balance are observed.

Dataset: Instances (samples): 195 Attributes (features): 22 + 1(label)

Focused on various acoustic properties of voice recordings, which are used to detect and analyze Parkinson's disease.

Voice measurements from 31 people, 23 with Parkinson's disease (PD).6 recordings per patient. The main aim is discriminate healthy=0 and PD=1 as binary classification.

How these measurements are performed:

Voice Recording: Subjects are typically asked to sustain a vowel sound (often 'ahhh') for several seconds.
Signal Processing: The voice recordings are then processed using specialized software that can extract these acoustic features.
MDVP: Many of these features are extracted using Multi-Dimensional Voice Program (MDVP), a software tool for voice analysis.
Advanced Analysis: Some of the more complex measures (like RPDE, D2, DFA) require advanced mathematical analysis of the voice signal.

5-Code Organization

The codes are separated into 7 different cells based on 5 steps. Read the descriptions and run the codes cell by cell. You can also run the whole code at once, if you want.

The steps are as follows:
    1) Importing the data
    2) EDA
    3) Preprocessing by 3 options. **Run 1 of them and compare the results.** 
    4) Model training and tuning
    5) Model usage

Cells are constructed by this command: #%% in Spyder IDE.