There is no doubt that the use of drugs has significant consequences for society, it introduces risk into the human life, increasing risk of poor health and greater earlier mortality and morbidity. The rapid growth of artificial intelligence and machine learning can provide useful tools for analyzing this problem; various methods have been given by researchers for increasing the prediction rate of drug users. This work proposes a decision support approach to investigate the relationship between drug user (year-based user definition) and personality traits. Psychologists approved the recent personality traits Five Factor Model (FFM) for understanding human individual differences. Two additional factors of personality are proven to be important for analysis of substance use, Impulsivity and Sensation-Seeking. The data of five factor personality profiles, Impulsivity and Sensation-Seeking, in addition to biographical data of 21 different types of legal and illegal drugs are depicted in tabular form and rough sets principles are applied to obtain all reducts and set of generalized rules are extracted to predict the drug user/Non-user (year-based user definition). The resultant set of classification rules performed with basic logic functions can be considered as knowledge base with high accuracy and may be valuable in many applications.
Digital Object Identifier (DOI)
A. Hares, Abdelbaset; A. mohamed, H.; and Abu Hawsah, Miad
"Decision Support Approach based Rough Sets Theory to Investigate the Relationship between Personality Traits and Drug User (Year-based Definition),"
Applied Mathematics & Information Sciences: Vol. 16:
6, Article 7.
Available at: https://digitalcommons.aaru.edu.jo/amis/vol16/iss6/7