  Early detection of Parkinson's disease (PD) is important which can enable
early initiation of therapeutic interventions and management strategies.
However, methods for early detection still remain an unmet clinical need in PD.
In this study, we use the Patient Questionnaire (PQ) portion from the widely
used Movement Disorder Society-Unified Parkinson's Disease Rating Scale
(MDS-UPDRS) to develop prediction models that can classify early PD from
healthy normal using machine learning techniques that are becoming popular in
biomedicine: logistic regression, random forests, boosted trees and support
vector machine (SVM). We carried out both subject-wise and record-wise
validation for evaluating the machine learning techniques. We observe that
these techniques perform with high accuracy and high area under the ROC curve
(both >95%) in classifying early PD and healthy normal. The logistic model
demonstrated statistically significant fit to the data indicating its
usefulness as a predictive model. It is inferred that these prediction models
have the potential to aid clinicians in the diagnostic process by joining the
items of a questionnaire through machine learning.
