Parkinson's disease detection using olfactory loss and REM sleep disorder features.

In Parkinson's disease, there exists a prodromal or a premotor phase characterized by symptoms like olfactory loss and sleep disorders, which may last for years or even decades before the onset of motor clinical symptoms. Diagnostic tools based on machine learning using these features can be very useful as they have the potential in early diagnosis of the disease. In the paper, we use olfactory loss feature from 40-item University of Pennsylvania Smell Identification Test (UPSIT) and Sleep behavior disorder feature from Rapid eye movement sleep Behavior Disorder Screening Questionnaire (RBDSQ), obtained from the Parkinson's Progression Marker's Initiative (PPMI) database, to develop automated diagnostic models using Support Vector Machine (SVM) and classification tree methods. The advantage of using UPSIT and RBDSQ is that they are quick, cheap, and can be self-administered. Results show that the models performed with high accuracy and sensitivity, and that they have the potential to aid in early diagnosis of Parkinson's disease.