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ZENODO
Dataset . 2026
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
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ARTIFICIAL INTELLIGENCE AND DIGITAL BIOMARKERS IN THE EARLY DIAGNOSIS AND PREDICTION OF PARKINSON'S DISEASE: A SYSTEMATIC REVIEW (2020–2025)

Authors: Atakhanov Sanjarbek Anvarovich; Abduraimova Jasmina Dilmurodovna; Mashrabjonova Marjona Davranbekovna;

ARTIFICIAL INTELLIGENCE AND DIGITAL BIOMARKERS IN THE EARLY DIAGNOSIS AND PREDICTION OF PARKINSON'S DISEASE: A SYSTEMATIC REVIEW (2020–2025)

Abstract

According to data from the STADA Health Report 2023, the disease that people in Uzbekistan fear the most is Parkinson's disease. This disease causes concern among 23% of the country’s population. These fears are not unfounded, as the disease is considered the second most common neurodegenerative disorder worldwide. Parkinson’s disease is a condition caused by a deficiency of dopamine-producing neurons in the substantia nigra, which manifests itself through movement disorders. This article presents various methods of applying artificial intelligence and digital biomarkers for the diagnosis of Parkinson’s disease. It discusses the possibility of early diagnosis of Parkinson’s disease using artificial intelligence methods. The paper proposes an approach for detecting the disease using video analysis of a patient’s smile as a digital biomarker, an artificial intelligence model for diagnosing Parkinson’s disease based on frequency anomalies in electroencephalography (EEG), and automatic early detection of Parkinson’s disease through the analysis of acoustic signals using classification algorithms based on the Recursive Feature Elimination (RFE) method.

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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average