Predictive Model of Psychoactive Drugs Consumption using Classification Machine Learning Algorithms

Abstract

It is difficult to predict the effect of drugs on the individuals, as its results are unpredictable and most often dangerous. For a police purpose that concerned with the protection of individuals, the problem of predicting drug abusing is highly important. A dataset was used from open-source website UCI, that includes specific attributes about using up of eighteen different psychoactive drugs. Our study aimed to use data mining classification techniques, in order to classify the individual into two categories: user or non-user. Eighteen classification models were built using different classification algorithms such as Gaussian Naive Bais, Logistic Regression, k-nearest neighbors, Random Forest, and Decision Tree. The accurate classifier was chosen by studying the accuracy, recall, precision, and f1-score measures for each one, and it was evaluated by the Holdout method. The results were obtained optimally, and we got 18 models, where each one had different high accurate outputs, that classify an individual to user and non-user. The final model is a combination of 18 models for 18 critical psychoactive drugs: Alcohol, Amphet, Amyl, Benzos, Caff, Cannabis, Choc, Coke, Crack, Ecstasy, Heroin, Ketamine, Legalh, LSD, Meth, Mushrooms, Nicotine and VSA. This study in turn may give a chance for the decision makers to reduce the risk of these drugs consumption, in order to avoid healthcare issues and keep the community in safe.