Project Abstract

This project aims to investigate the application of machine learning techniques to predict Parkinson’s disease using keystroke patterns. Parkinson’s disease is a neurodegenerative disease that affects motor function, and early detection is critical for effective treatment and management. Conventional diagnostic methods have limitations and non-invasive and readily available approaches are needed.Keystroke patterns that describe the timing and dynamics of typing have shown potential as a biomarker for Parkinson’s disease. This project collects keystroke data from people with and without Parkinson’s disease and pre-processes it for analysis. Then machine learning algorithms such as support vector machines, decision trees are trained and evaluated using keystroke data.The project will also consider the interpretability of the models and identify keystroke characteristics that are most informative for predicting Parkinson’s disease. The findings help to understand the relationship between keystrokes and Parkinson’s disease and provide insights for future research and development of diagnostic tools.The significance of this project is that, with machine learning and readily available keystrokes, it could revolutionize the diagnosis of Parkinson’s disease. The non-invasive nature of the approach and the ability to collect data remotely make it a promising tool for population-level screening and early intervention. Early detection can lead to timely treatment and improve the quality of life of people with Parkinson’s disease.Successful implementation of this machine learning project could ultimately contribute to the early detection and diagnosis of Parkinson’s disease, enabling timely detection and improved patient outcomes.

Keywords: Machine Learning and Algorithm, Parkinson’s Disease, Keystroke Data

 Conference Details

Session: Presentation Stream 16 at Presentation Slot 8

Location: GH001 at Wednesday 8th 09:00 – 12:30

Markers: Arno Pauly, Alec Critten (GTA)

Course: MSc Advanced Computer Science, Masters PG

Future Plans: I’m looking for work