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Journals / Turkish Journal of Electrical Engineering and Computer Science / 2014 / Cilt: 22 - Sayı: 3

An urgent precaution system to detect students at risk of substance abuse through classification algorithms

Faruk BULUT
İhsan Ömür BUCAK
Pages
690–707
Publication Date
2014-05-16
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The findings of this study show that if there is a computed high risk for a teenager, some precautions, if necessary, may be taken by educators and parents to keep the teenager away from those substances.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In recent years, the use of addictive drugs and substances has turned out to be a challenging social problem worldwide.
📊
Key Findings & Data:The illicit use of these types of drugs and substances appears to be increasing among elementary and high school students.
💡
Conclusion & Impact:The findings of this study show that if there is a computed high risk for a teenager, some precautions, if necessary, may be taken by educators and parents to keep the teenager away from those substances.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Substance abuse · risk assessment · data mining

Abstract

In recent years, the use of addictive drugs and substances has turned out to be a challenging social problem worldwide. The illicit use of these types of drugs and substances appears to be increasing among elementary and high school students. After becoming addicted to drugs, life becomes unbearable and gets even worse for their users. Scientific studies show that it becomes extremely difficult for an individual to break this habit after being a user. Hence, preventing teenagers from addiction becomes an important issue. This study focuses on an urgent precaution system that helps families and educators prevent teenagers from developing this type of addiction. The aim of this study is to detect a teenager's probability of being a drug abuser using classification algorithms in machine learning and data mining. The objective is not to test the classifiers theoretically on the benchmark datasets, but rather to use this study as a basis for advanced and detailed studies in this field in the future. This paper not only uses a special dataset but also focuses on psychometrics and statistics. The findings of this study show that if there is a computed high risk for a teenager, some precautions, if necessary, may be taken by educators and parents to keep the teenager away from those substances.

Özet

In recent years, the use of addictive drugs and substances has turned out to be a challenging social problem worldwide. The illicit use of these types of drugs and substances appears to be increasing among elementary and high school students. After becoming addicted to drugs, life becomes unbearable and gets even worse for their users. Scientific studies show that it becomes extremely difficult for an individual to break this habit after being a user. Hence, preventing teenagers from addiction becomes an important issue. This study focuses on an urgent precaution system that helps families and educators prevent teenagers from developing this type of addiction. The aim of this study is to detect a teenager's probability of being a drug abuser using classification algorithms in machine learning and data mining. The objective is not to test the classifiers theoretically on the benchmark datasets, but rather to use this study as a basis for advanced and detailed studies in this field in the future. This paper not only uses a special dataset but also focuses on psychometrics and statistics. The findings of this study show that if there is a computed high risk for a teenager, some precautions, if necessary, may be taken by educators and parents to keep the teenager away from those substances.

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Cite This Article

BULUT, F., BUCAK, İ. Ö. (2014). An urgent precaution system to detect students at risk of substance abuse through classification algorithms. Turkish Journal of Electrical Engineering and Computer Science, Cilt: 22 - Sayı: 3, 690–707. https://ajindex.com/articles/188454

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