Substance use prediction using artificial intelligence techniques

Substance use poses a significant public health challenge worldwide, including in Finland. This study seeks to predict patterns of substance use, aiming to identify the driving factors behind these trends using artificial intelligence techniques. This research utilizes data from the 2022 Finnish National Drug Survey, comprising 3,857 participants, to develop predictive models targeting the use of cannabis, ecstasy, amphetamine, cocaine, and non-prescribed medications. Analysis of 23 questionnaire items yielded 76 features across four substance use dimensions: demographic attributes, experience and preferences of drug use, health-related aspects of drug use, and social attributes of drug use. In addition to traditional machine learning (ML) approaches previously applied in this field, three sophisticated deep learning models—standard LSTM, BiLSTM, and Recursive LSTM—were employed to evaluate their predictive performance. These LSTM models were further augmented with SHAP analysis to identify the primary influences on substance use patterns. While all these artificial intelligence models demonstrated superior predictive performance, our focus was specifically on the outcomes of the LSTM models due to their novel application in this field. The results underscore the exceptional performance of both LSTM and ML models in unraveling complex substance use behaviors, underlining their applicability in diverse public health contexts. This study not only sheds light on the predictors of substance uses but also furthers methodological innovation in drug research, charting new directions for crafting targeted intervention strategies and policies. The observed variability in predictor significance across different substances indicates the necessity for tailored prevention programs catering to particular user groups. Integrating machine learning with social science and public health policy, our research deepens the understanding of the factors influencing substance use and promotes effective strategies for its mitigation. Despite some limitations, this investigation establishes a foundation for future studies and accentuates the critical role of advanced computational techniques in addressing intricate social issues.