Accurate early diagnosis of Parkinson's disease is essential. Using data available from the Parkinson's Progression Markers Initiative study, we identified a multivariate logistic regression model including cerebrospinal fluid α-synuclein, olfactory function, age, and gender that achieved a high degree of discrimination between patients with Parkinson's disease and healthy control or scan without evidence of dopaminergic deficit participants. Additionally, the model could predict the conversion of scan without evidence of dopaminergic deficit to Parkinson's disease, as well as discriminate between normal and impaired subjects with leucine-rich repeat kinase 2 mutations. Although further validation is needed, this model may serve as an alternative method to neuroimaging screening in Parkinson's disease studies.