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Parkinson Detection From Voice Data (Part1 iBest Workshop)

BeginnerGuided Project

This Guided Project will provide an introduction to Artificial Intelligence and Machine Learning using Python and Scikit-Learn. Through it, learners will learn how to use Python and Scikit-Learn to build a Machine Learning model to accurately detect Parkinson’s Disease from voice patterns. By the end of this project, you will have gained the skills needed to start building your own AI-powered predictions.

4.8 (101 Reviews)

Language

  • English

Topic

  • Artificial Intelligence

Industries

  • Healthcare

Enrollment Count

  • 419

Skills You Will Learn

  • Artificial Intelligence, Python, Machine Learning

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 30 min

Platform

  • SkillsNetwork

Last Update

  • May 12, 2025
About this Guided Project
This project aims to leverage machine learning techniques to analyze voice recordings and detect the presence of Parkinson's disease, a neurological disorder that affects movement. The goal is to develop a model that can accurately predict the disease using voice data, which could help in the early detection and treatment of the condition.

To achieve this objective, the project will involve the use of Python for data analysis and machine learning. Machine learning algorithms such as decision trees and support vector machines will be implemented to analyze voice features and make predictions about the presence of Parkinson's disease. The model will be evaluated for performance metrics.

In addition to implementing machine learning algorithms, the project will also involve conducting a grid search for tuning the parameters of the model. This step is essential for optimizing the performance of the model and improving its predictive power. Visualizing the decision tree model will also be part of the project, which can help in interpreting the results and identifying important features.

A Look at the Project Ahead

objectives for the project "Using Machine Learning to Analyze Voice Disorders for Parkinson's Disease Detection":
  1. Develop a machine learning model that can accurately predict the presence of Parkinson's disease based on voice recordings.
  2. Implement different machine learning algorithms such as decision trees and support vector machines to analyze voice features and make predictions.
  3. Conduct a grid search to optimize the parameters of the model and improve its predictive power.
  4. Visualize the decision tree model to aid in interpreting the results and identifying important features.

What You'll Need

Your enthusiasm and a browser. This project runs on IBM skills' network cloud, and there is no need for any local installation.

Instructors

Sina Nazeri

Data Scientist at IBM

I am grateful to have had the opportunity to work as a Research Associate, Ph.D., and IBM Data Scientist. Through my work, I have gained experience in unraveling complex data structures to extract insights and provide valuable guidance.

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Joseph Santarcangelo

Senior Data Scientist at IBM

Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

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Alice Rueda

Postdoctoral Fellow

Alice is a postdoctoral fellow and AI Lead at the Interventional Psychiatry Program, St. Michael’s Hospital and iBEST Trainee Lead. Alice completed her doctoral degree in electrical engineering from Toronto Metropolitan University (formerly Ryerson University), Toronto, ON in 2021. After working in the industry for more than a decade, I decided to pursuit my doctoral degree in 2016. I received a bachelor degree in electrical engineering and a master degree in electrical and computer engineering from the University of Manitoba, Winnipeg, MB in 1994 and 1999, respectively. I was awarded an (honoris causa) Doctor of Laws degree from Brock University, St. Catharines, ON in 2020. I specialize in signal processing and applications of machine learning. I am currently serving as the Secretary for the IEEE Signal Processing Toronto Chapter, an affiliated member of the IEEE Machine Learning for Signal Processing, and a reviewer for IEEE conferences. Alice had also served as the Director of Machine Learning at Aggregate Intellect in 2022.

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