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Parkinson's disease (PD) is debilitating, progressive, and clinically markedby motor symptoms. As the second most common neurodegenerative disease in theworld, it affects over 10 million lives globally. Existing diagnoses methodshave limitations, such as the expense of visiting doctors and the challenge ofautomated early detection, considering that behavioral differences in patientsand healthy individuals are often indistinguishable in the early stages.However, micrographia, a handwriting disorder that leads to abnormally smallhandwriting, tremors, dystonia, and slow movement in the hands and fingers, iscommonly observed in the early stages of PD. In this work, we apply machinelearning techniques to extract signs of micrographia from drawing samplesgathered from two open-source datasets and achieve a predictive accuracy of94%. This work also sets the foundations for a publicly available anduser-friendly web portal that anyone with access to a pen, printer, and phonecan use for early PD detection.

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