In many medical emergencies, such as a Stroke, survivability requires fast diagnosis and treatment. But diagnosis may depend on a test that uses bulky, expensive equipment, such as the radiological imaging test that serves as a “gold standard” stroke test. That test is impractical in the field though, so a reliable portable test would be of great value. Data science offers a solution. Through the information embedded in a biological quantity known as gene expression, a data model can efficiently classify whether a patient is currently undergoing a stroke. This blog will discuss, specifically, the use of k-Nearest Neighbors (KNN) and Principal Component Analysis (PCA) to isolate a small number of genes whose combined expression levels might indicate a stroke is in progress. This can provide an alternative way to Identify stroke victims, with lower equipment requirements than traditional radiological imaging.