Risk prediction model for cannabis use with artificial intelligence approach
Ali Ünlüa b✉, Pekka Hakkarainena, Karoliina Karjalainena, Abdülhamit Subaşıc d
- aAlcohol, Drugs and Tobacco Unit, Finnish Institute for Health and Welfare (THL), Helsinki, Finland
- bDepartment of Computer Science, Aalto University, Espoo, Finland
- cDepartment of Computer Science, College of Engineering, Effat University, Jeddah, Saudi Arabia
- dInstitute of Biomedicine, Faculty of Medicine, University of Turku, Turku, Finland
- ✉Corresponding author
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Abstract
Background Identifying the most important predictors of substance use is crucial for developing effective prevention policies. Traditional statistical methods have some limitations in this regard. To address these limitations, the researchers utilized artificial intelligence (AI) methods to identify the top 10 most important predictors of cannabis use in Finland.Objective The objective of this study was to apply AI techniques to identify the key predictors of cannabis use in Finland. Specifically, the researchers aimed to determine the top 10 most important features related to cannabis use from a dataset consisting of 3229 observations and 313 questionnaire items, with 48 selected for preprocessing.Methods The researchers employed the recursive feature elimination (RFE) method as part of their AI analysis. This technique was used on 60 processed variables, following the application of missing data imputation, resampling, and scaling techniques. The RFE method allowed the researchers to narrow down the 60 variables to the top 10 most important features associated with cannabis use.Results The AI models developed using the selected features were able to predict cannabis use with a remarkable accuracy of 96% for the previous 12 months. The results of the study revealed that the social settings of individuals played the most significant role in predicting cannabis use in the context of Finland.Conclusions In conclusion, this study demonstrated the effectiveness of AI-based approaches in identifying the most critical predictors of cannabis use in Finland. The research highlighted that social settings had the highest impact on cannabis use in this setting. Moreover, the study showcased the potential of AI methods not only for identifying key risk indicators among various factors but also for optimizing the utilization of limited public resources when devising prevention strategies. These findings can be valuable for shaping targeted and efficient prevention policies to address cannabis use in Finland.
TL;DRThe research highlighted that social settings had the highest impact on cannabis use in this setting and showcased the potential of AI methods not only for identifying key risk indicators among various factors but also for optimizing the utilization of limited public resources when devising prevention strategies. (Semantic Scholar)