Application of Machine Learning to Parkinson’s Disease Diagnosis

Machine learning is one of the research hotspots and focuses of medical artificial intelligence. For the early diagnosis of neurodegenerative Parkinson’s Disease (PD), the existing clinical rating scales have certain subjectivity and limitations. This paper reports the research progress of machine learning in the diagnosis of PD based on behavioral (speech, gait, and writing), electrophysiology (Electroencephalogram, EEG), radiomics (magnetic resonance imaging, single-photon emission tomography, and positive photon emission tomography), and genomics data. The report finds that the application of machine learning is more accurate than the traditional method in the diagnosis of PD, which provides reference for the research and application of artificial intelligence intelligent diagnosis in the future.