Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature.

ABSTRACT: Diagnosis of Parkinson's disease (PD) is commonly based on medical observations and assessment of clinical signs, including the characterization of a variety of motor symptoms. However, traditional diagnostic approaches may suffer from subjectivity as they rely on the evaluation of movements that are sometimes subtle to human eyes and therefore difficult to classify, leading to possible misclassification. In the meantime, early non-motor symptoms of PD may be mild and can be caused by many other conditions. Therefore, these symptoms are often overlooked, making diagnosis of PD at an early stage challenging. To address these difficulties and to refine the diagnosis and assessment procedures of PD, machine learning methods have been implemented for the classification of PD and healthy contro

SUBMITTER: Mei J 

PROVIDER: S-EPMC8134676 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature