Means of intellectual data analysis and support of decision-making in diagnostics and treatment of drug-dependent
Early detection of the drug used by the patient is essential In the diagnosis and treatment of drug addicts. There are specific symptoms of drug use, according to which it is determined that the patient used before the laboratory tests. The use of methods of data mining allows you to identify the characteristic signs of using several drugs and establish previously unexplored symptoms for new drugs, identify typical and atypical patients. In the work, the patterns between the narcotic drugs used and the symptoms are mathematically described using associative rules. Algorithms Apriori, Close and the MClose algorithm proposed by the authors are used to find these rules. The MClose algorithm finds the most significant strict associative rules (rules with reliability 1). The article presents a proposal on expert pre-pro- cessing of melon, which allows to significantly reduce the number of generated associative rules and improve the quality of their interpretation. The developed methods and means is aimed at diagnosing and supporting decision-making in the treatment of drug addicts.
Decision support systems