This paper will investigate viability of a screening application that could be used to identify individuals with Dysarthria from among a larger population using sentence-level speech data. This task presents a number of challenged particularly if we aim to identify the disorder in the earlier stages before the more significant symptoms have begun to manifest themselves. A principal challenge in this task is acheiving robustness to the large number of confounding variables such as gender, age, accent, speaking style, and recording conditions. All of these variables will affect an individuals speech in a manner unrelated to the disorder, and identifying what information is relevant to the disorder amongst these confounding variables given the limited amount of data that is available in this regime presents a major engineering challenge. In this paper we will focus on achieving robustness to different types and levels of noise by employing a feature selection algorithm that attempts to minimize a non-parametric upper bound of the error in the noisy condition. This is a crucial problem to solve as the clean recording conditions used in data collection are typically a poor reflection of the type of data that will be encountered upon deployment.