Machine Learning for Real-Time, Automatic, and Early Diagnosis of Parkinson's Disease by Extracting Signs of Micrographia from Handwriting Images

TL;DR

This paper presents a machine learning approach to detect early Parkinson's disease signs from handwriting images, achieving high accuracy and aiming to provide accessible early diagnosis tools.

Contribution

It introduces a novel machine learning method for early PD detection based on micrographia signs from handwriting, with a publicly accessible web portal prototype.

Findings

Achieved 94% predictive accuracy in early PD detection

Utilized open-source datasets for handwriting analysis

Developed a user-friendly web portal for early diagnosis

Abstract

Parkinson's disease (PD) is debilitating, progressive, and clinically marked by motor symptoms. As the second most common neurodegenerative disease in the world, it affects over 10 million lives globally. Existing diagnoses methods have limitations, such as the expense of visiting doctors and the challenge of automated early detection, considering that behavioral differences in patients and healthy individuals are often indistinguishable in the early stages. However, micrographia, a handwriting disorder that leads to abnormally small handwriting, tremors, dystonia, and slow movement in the hands and fingers, is commonly observed in the early stages of PD. In this work, we apply machine learning techniques to extract signs of micrographia from drawing samples gathered from two open-source datasets and achieve a predictive accuracy of 94%. This work also sets the foundations for a publicly available and user-friendly web portal that anyone with access to a pen, printer, and phone can use for early PD detection.