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Identifying Risk Factors of Substance Use in Finland: A Machine Learning Approach.

Ali Ünlü1,2, Pekka Hakkarainen3, Karoliina Karjalainen3

  • 1School of Education and Human Development, Research Scientist, University of Virginia, Charlottesville, Virginia, USA.

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Summary
This summary is machine-generated.

Artificial Intelligence (AI) and Machine Learning identify 23 risk factors for substance use in Finland. Predictive models like Bidirectional Long Short-Term Memory (BiLSTM) show promise for targeted prevention strategies.

Keywords:
BiLSTMFinlandRisk factordeep learningfeature selectionmachine learningsubstance use

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Area of Science:

  • Public Health
  • Data Science
  • Epidemiology

Background:

  • Explores substance use dynamics in Finland.
  • Employs Artificial Intelligence (AI) and Machine Learning (ML) for pattern identification.
  • Utilizes data from the Finnish National Drug Survey.

Purpose of the Study:

  • Identify key risk factors for drug use.
  • Predict patterns of illicit substance consumption.
  • Inform targeted prevention strategies and policy interventions.

Main Methods:

  • Applied 15 feature selection methodologies.
  • Analyzed data on five major illicit substances: cannabis, ecstasy, amphetamines, cocaine, and non-medical prescription drugs.
  • Utilized the Bidirectional Long Short-Term Memory (BiLSTM) model for predictive analysis.

Main Results:

  • Identified 23 significant risk factors for substance use.
  • Common risk factors include e-cigarette consumption, drug offers, and health problems.
  • The BiLSTM model demonstrated promising accuracy in predicting substance use.

Conclusions:

  • AI integration with epidemiological data provides valuable public health insights.
  • Highlights the complexity of substance use behaviors.
  • Predictive analytics can enhance prevention efforts in Finland.