Authors:

Tomer Zamir and Max Melichov

In this study, we aimed to explore the potential of using machine learning to analyze changes in the voice that may be associated with PD. By doing so, we hoped to build a model that could accurately diagnose the disease.

Research Questions:

• Could machine learning be used to accurately diagnose Parkinson's disease based on changes in the voice?
• Which acoustic features did PD affect the most?
• What was the best method to diagnose PD?

We proposed that by analyzing features of the voice, we could build a model to accurately diagnose PD. Our research has confirmed that the model is capable of identifying relevant patterns in the voice data that indicate PD and distinguishing between individuals with and without the disease.

An overview and bibliography for the project can be found in the presentation file.