Building a Heroin Abuse Prediction Model: A Generalized Machine Learning Approach

Aims: In the modern era, substance abuse is a global problem. CDC’s (Centre for Disease Control) National Center for Health Statistics reported in July 2021 that, more than 93000 drug overdose deaths occurred alone in the United States. Design: A cohort study. Setting: The drug consumption data is collected online in United Kingdom. Total 2000 records are present, out of which, 1885 records are considered for the work after initial data processing. The dataset has the drug consumption output outcome of 18 drugs, heroin is one of them. Measurements: In this work, an intelligent approach has been proposed for detecting abuse of one of the most illicit substances - heroin. A random forest-based machine learning model is proposed which can predict heroin abused individuals with very high accuracy. For the abuse prediction, various supervised machine learning methods are applied, and their performance is compared. These algorithms are applied to different sets of features. Among the applied five algorithms, feature importance score is calculated for logistic regression, ensemble learning (gradient boosting) and random forest. The feature importance score for each feature is calculated for all applied algorithms k-nearest neighbour and naïve bayes. The ranking of all features is done based on the obtained score. Findings: It is found in the study that the heroin-abuse dataset collected from UCI, two-classification based on random forest (RF) and gradient boosting (GB) achieved more than 90% accuracy as well as more than 90% sensitivity, specificity, precision, and f-score. The proposed model gave the best accuracy 94.697% for heroin-use detection. Conclusions: The assessment of the proposed framework on all possible feature sets shows that the framework works well for heroin abuse prediction as different give satisfactory results.