Using a Nearest Neighbor Algorithm and Logistic Regression to Assess Hazard Identification in the U.S. Army Risk Management Process
This research considers whether a person's demographic and experiential attributes play a significant role in how they perceive the presence or absence of hazards in a given situation. The goal of the research is to show that participants with enlisted military experience, prior to being commissioned as a junior officer, would be more successful at identifying the hazards presented in military scenarios than those who had only been trained on the process via their pre-commissioning and initial entry courses of instruction. The research study involves the use of two surveys with realistic military scenarios including both Foot March and Maintenance scenarios. The data collected from the surveys was analyzed using data mining techniques, in particular Nearest Neighbor (NN) algorithm and Logistic Regression Model (LRM). NN determines how similar a participant's case is to an expert case and LRM analyzes the outputs in a way that allows us to see if any of the seven experiential and demographic attributes considered had a significant impact on a participant's ability to perform well on the assessment. While the results did not conclusively prove that experience or other demographic attributes had a statistically significant impact on a participant's overall performance, the results did suggest that the idea that those same attributes do not have an impact cannot be rejected. This research could provide useful feedback to the U.S. Army on the way they train and educate junior officers on their Risk Management process.
Nearest Neighbor Algorithm, Logistic Regression Model, LRM, Risk Management, Hazards, Computer Engineering, Other Computer Sciences
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