Parkinnson's Detector

Project idea

Having a passion for Software Engineering in the medical field, I chose to look at a project focusing on using Machine Learning to train a model to differentiate a healthy person from a person with Parkinson's Disease using several biomedical voice measurements of a given person. The specific model used was Gradient Boosting (more information in the 'How the model was trained' section below). The data source is also listed below.

The aim is to help diagnose people with Parkinson's disease early so that treatments can be carried out early (and hence increasing the chance of success). This early diagnosis can be done through analysing the participitant's vocal frequencies. Due to these input being relatively ease to get, the model makes early diagnosis of Parkinson's disease easier.

Although the model is complete and a relatively good accuracy on the model is achieved, due to time restrictions and intermediate knwoledge of ML models, further tuning of the graident boosting model and analysis of the input varaibles significance have not completely been carried out. Hence, further research can go into these fields to improve the model.

Project background

This project is part of the submission for the Artificial intelligence project for Microsoft Student Accelerator program (Australia)

Code Dependencies and Technologies used

Language used:

• Python (version 3.7)

To run the code file successfully, ensure that the following libraries are installed:

• Pandas
• Scikit-learn
• Numpy

Also ensure that the 'parkinsons.data' csv file is downloaded and is in the working directory

Data Source

The data was sourced from the following research paper: 'Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection', Little MA, McSharry PE, Roberts SJ, Costello DAE, Moroz IM. BioMedical Engineering OnLine 2007, 6:23 (26 June 2007). It can be downloaded from https://archive.ics.uci.edu/ml/datasets/parkinsons

There are 195 rows and 23 columns in the raw data set. The data is also slightly biased in that 23 of the 31 participating people tested for their vocal measurements had Parkinnson's Disease. There was the option of removing some of the data sets relating to people with parkinson's Disease, to lessen this biasedness, however, this would reduce the already small data set, so this option was not exercised.

How the model was trained

Since the algorithm is about predicting 2 possible outcomes, True or False (i.e. it's a classification problem) and the data set is quite small (only 195 rows), I decided to use Gradient boosting to build my model. Not all variables from the raw data set were used in the final model (selection was based on omitting insignificant variables and repetetive testing) The final model was chosen based on it's accuracy and ROC_AUC value.

How to test the model

• Ensure the correct environment is set-up (see 'Code Dependencies and Technologies used' section)
• Ensure the parkinsons.data csv file is in the working directory
• Open the main.py file in your IDE
• The code includes a function called has_parkinson() with function definition/description included in the comments
• The last 5-6 lines can be used to test the function manually (more instruction in the code comments)
• To print model statistics, use the function disp_model_stats() at line 83 (in the original main.py file)

Model statistics

The following metrics were recorded for the final model:

• Accuracy: 92.31%
• ROC_AUC: 96.37%
• Number of independent variables: 10 (initial data set had 23 attributes)

How to test the model?

References: The background to understand the relation between vocal measurements and Parkinson's disease was gained from the following research paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5549905/

Author/s:

Kunj Dave