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The review aims to summarize and analyze studies on machine learning methods for the detection of Parkinson's disease.

March 6, 2026AlgorithmsOpen Access

Parkinson’s Disease Detection Using Machine Learning Algorithms: A Comprehensive Review

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Comprehensive review analyzes machine learning methods for detecting Parkinson’s disease, highlighting challenges and progress.

The review aims to summarize and analyze studies on machine learning methods for the detection of Parkinson's disease.
Synthesized 32 studies on Parkinson's disease detection published from 2017 to 2025.
Analyzed diverse data modalities including MRI, EEG, and voice recordings.
Assessed performance of machine learning and deep learning algorithms in clinical applications.
High classification performance reported, particularly with CNN-based and hybrid deep learning methods.
Classical machine learning techniques like SVM and ensemble models show competitive results.
Identified barriers include small datasets and inconsistent evaluation protocols, affecting reliable translation.

Cincović et al. (2026) studied this question.

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