Machine Learning Approaches in Parkinson’s Disease

Title:Machine Learning Approaches in Parkinson’s Disease

Volume: 28 Issue: 32

Author(s): Annamaria Landolfi*, Carlo Ricciardi, Leandro Donisi, Giuseppe Cesarelli, Jacopo Troisi, Carmine Vitale, Paolo Barone and Marianna Amboni

Affiliation:

• Department of Medicine, Surgery and Dentistry, “Scuola Medica Salernitana” Neuroscience section, University of Salerno, Baronissi (SA),Italy

Keywords: Machine learning, Parkinson disease, metabolomics, gait analysis, neuroimaging, speech analysis, handwriting analysis.

Abstract:

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 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 machine learning approaches can help clinicians in classifying patients according to several variables at the same time.

Cite this article as:

Landolfi Annamaria *, Ricciardi Carlo , Donisi Leandro , Cesarelli Giuseppe , Troisi Jacopo , Vitale Carmine , Barone Paolo and Amboni Marianna , Machine Learning Approaches in Parkinson’s Disease, Current Medicinal Chemistry 2021; 28 (32) . https://dx.doi.org/10.2174/0929867328999210111211420