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Background and aim: Parkinson's disease is a neurodegenerative disease. It is often diagnosed at an advanced stage, which can influence the control over the illness. Therefore, the possibility of diagnosing Parkinson's disease at an earlier stage, and possibly prognosticate it, could be an advantage. Given this, a literature review that covers current studies in the field is relevant. Methods: The aim of this study is to present a systematic literature review in which the models used for the diagnosis and prognosis of Parkinson's disease through voice and speech assessment are elucidated. Three databases were consulted to obtain the studies between 2019 and 2023: SienceDirect, IEEE Xplore and ACM Library. Results: One hundred and six studies were considered eligible, considering the definition of inclusion and exclusion criteria. The vast majority of these studies (94.34%) focus on diagnosing the disease, while the remainder (11.32%) focus on prognosis. Conclusion: Voice analysis for the diagnosis and prognosis of Parkinson's disease using machine learning techniques can be achieved, with very satisfactory performance results, like is demonstrated in this systematic literature review.
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Funding Information:
This work was partially funded by FCT/MCTES through national funds and co-funded by the FEDER–PT2020 partnership agreement under the project UIDB/50008/2020 of Instituto de Telecomunicações. This article is based on work from COST Action IC1303–AAPELEArchitectures, Algorithms and Protocols for Enhanced Living Environments and COST Action CA16226–SHELD-ON–Indoor living space improvement: Smart Habitat for the Elderly, supported by COST (European Cooperation in Science and Technology).
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© 2025 The Authors
Palavras-chave
Diagnosis or prognosis Machine learning Parkinson's disease Voice or speech Medicine (miscellaneous) Artificial Intelligence