Background: Parkinson’s disease is the second most frequent neurodegenerative
disorder. Its diagnosis is challenging and mainly relies on clinical aspects. At present,
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 characterization.
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 application
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 excluding
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
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 machine
learning approaches can help clinicians in classifying patients according to several
variables at the same time.