Parkinson’s disease (PD) is a neurodegenerative disorder that causes severe motor and cognitive dysfunctions. Several types of physiological signals can be analyzed to accurately detect PD by using machine learning methods. This work considers the diagnosis of PD based on voice patterns. In particular, we focus on assessing the performance of eight different pattern ranking techniques (also termed feature selection methods) when coupled with nonlinear support vector machine (SVM) to distinguish between PD patients and healthy control subjects. The parameters of the radial basis function kernel of the SVM classifier were optimized by using Bayesian optimization technique. Our results show that the receiver operating characteristic and the Wilcoxon-based ranking techniques provide the highest sensitivity and specificity.