Parkinson’s Disease (PD) is the second most common neurodegenerative disorder. It is a disease with a broad spectrum of symptoms, both motor and non-motor, but is often only diagnosed when the motor symptoms begin to appear. By this time however, a large amount of the dopaminergic neurons of the substantia nigra pars compacta have already deteriorated. It is therefore of great interest to be able to diagnose the disease earlier on in its progression and perhaps slow down or halt its course. Recent literature has supported the idea that non-motor symptoms begin to appear years, perhaps even decades, before the motor symptoms are visible. This makes them a prime candidate for diagnosing PD earlier on. With the aim of assessing the prevalence of different NMS in prodromal and early Parkinson’s, 19 studies addressing different NMS were analyzed. It was found that NMS are prevalent in both prodromal and early PD. The strongest prodromal predictors for PD were found to be olfactory dysfunction and REM-sleep behavior disorder (RBD).

Parkinson's disease (PD) is a neurodegenerative disease that affects motor abilities with increasing severity as the disease progresses. Traditional methods for diagnosing PD require specialists scoring qualitative symptoms using the motor subscale of the Unified Parkinson's Disease Rating Scale (UPDRS-III). Using force-plate data during quiet standing (QS), this study uses machine learning to target the characterization and prediction of PD and UPDRS-III. The purpose of predicting different subscores of the UPDRS-III is to give specialists more tools to help make an informed diagnosis and prognosis. The classification models employed classified PD with a sensitivity of 87.5% and specificity of 83.1%. Stepwise forward regression indicated that features correlated with base of support were most useful in the prediction of head rigidity (r-square = .753). Although there is limited data, this thesis can be used as an exploratory study that evaluates the predictability of UPDRS-III subscores using QS data. Similar prediction models can be implemented to a home setting using low-cost force plates as a novel telemedicine technique to track disease progression.