Weapon-Carrying among High School Students: A Predictive Model Using Machine Learning

Abstract

This study is aimed at 1) identifying the predictors for weapon-carrying on school properties; 2) build a predictive model for parents, educators, and pediatricians for early intervention. Youth Risk Behavior Surveillance System (YRBSS) 2017 data were used for this study. Logistic regression model is used to calculate the predicted risk. Logistic regression is a part of a category of statistical models called generalized linear models, and it allows one to predict a discrete outcome from a set of variables that may be continuous, discrete, dichotomous, or a combination of these. Typically, the dependent variable is dichotomous and the independent variables are either categorical or continuous. The data is run through R program. The outcome variable is weapon-carrying based Q13 (During the past 30 days, on how many days did you carry a weapon such as a gun, knife, or club on school property?) The result identified several important predictors for carrying weapon on school properties, such as gender, alcohol use, and smoking age. This provided important information for the educators and parents for early intervention and alleviating the negative effects of weapon-carrying among teenagers.

Keywords: Weapon-Carrying, High School, Students