Machine Learning Approaches in Parkinson's Disease

Background: Parkinson's disease is the second most frequent neurodegenera-tive disorder. Its diagnosis is challenging and mainly relies on clinical aspects. At pre-sent, no biomarker is available to obtain a diagnosis of certainty in vivo. Objective: The present review aims at describing machine learning algorithms as they have been variably applied to different aspects of Parkinson's disease diagnosis and char-acterization. Methods: A systematic search was conducted on PubMed in December 2019, resulting in 230 publications obtained with the following search query: Machine Learning AND Parkinson Disease. Results: The obtained publications were divided into 6 categories, based on different ap-plication fields: Gait Analysis -Motor Evaluation, Upper Limb Motor and Tremor Evaluation, Handwriting and typing evaluation, Speech and Phonation evaluation, Neuroimaging and Nuclear Medicine evaluation, Metabolomics application, after ex-cluding the papers of general topic. As a result, a total of 166 articles were analyzed after elimination of papers written in languages other than English or not directly related to the selected topics. Conclusion: Machine learning algorithms are computer-based statistical approaches that can be trained and are able to find common patterns from big amounts of data. The ma-chine learning approaches can help clinicians in classifying patients according to several variables at the same time.

Automated summary

Diagnosis and treatment of Parkinson's disease is a challenging issue, with machine learning algorithms being widely applied and developed for the diagnosis and characterization of Parkinson's disease, helping to find common patterns from large amounts of data.