Arcade-style sports games had quite the following in the early and mid-2000s. From Tony Hawk's Pro Skater to the many iterations of FIFA and NFL titles, these games had lasting impact on players, with many continuing to play new games or seeing remakes or remasters of their favorites. One such series was SSX, the popular snowboarding game that was released just over 20 years ago. SSX was the first title from EA Sports Big, a new-at-the-time addition to the EA developer umbrella. The game received critical acclaim across the board and received several awards, including 2001 Console Game of the Year from the Academy of Interactive Arts and Sciences.

In this report we examine the effectiveness of WISER in identification of a chemical culprit during a chemical based Mass Casualty Incident (MCI). We also evaluate and compare Binary Decision Tree (BDT) and Artificial Neural Networks (ANN) using the same experimental conditions as WISER. The reverse engineered set of Signs/Symptoms from the WISER application was used as the training set and 31,100 simulated patient records were used as the testing set. Three sets of simulated patient records were generated by 5%, 10% and 15% perturbation of the Signs/Symptoms of each chemical record. While all three methods achieved a 100% training accuracy, WISER, BDT and ANN produced performances in the range of: 1.8%-0%, 65%-26%, 67%-21% respectively. A preliminary investigation of dimensional reduction using ANN illustrated a dimensional collapse from 79 variables to 40 with little loss of classification performance.