A Model for Predicting the Class of Illicit Drug Suspects and Offenders

Abstract

In this study, the artificial neural network was deployed to develop a classification model for predicting the class of a drug-related suspect into either the drug peddler or non-drug peddler class. A dataset consisting of 262 observations on drug suspects and offenders in central Nigeria was used to train the model which uses parameters such as exhibit type, suspect’s age, exhibit weight, and suspect’s gender to predict the class of a suspect, with a predictive accuracy of 83%. The model sets the pace for the implementation of a full system for use at airports, seaports, police stations, and by security agents concerned with drug-related matters. The accurate classification of suspects and offenders will ensure a faster and correct reference to the sections of the drug law that correspond to a particular offence for appropriate actions such as prosecution or rehabilitation.