PMID: 9533732Apr 9, 1998Paper

Artificial neural networks for drug vulnerability recognition and dynamic scenarios simulation

Substance Use & Misuse
M BuscemaR Bricolo

Abstract

Semeion researchers have developed and used different kinds of Artificial Neural Networks (ANN) in order to process selected, "standard" data coming from drug users and from people who never used drugs before. In the first step a collection of 112 general variables, not traditionally connected to drug user's behavior, were collected from a sample of 545 people (223 heroin addicted and 322 non-users). Different types of ANNs were used to test the capability of the system to classify the drug users and the non-drug users correctly. A special ANN tool, created by Semeion, was also used to prune the number of the independent variables. The ANN selected for this first experiment was a Supervised Feed Forward Network, whose equations were enhanced by Semeion researchers. For the validation of the capability of generalization of the ANN, the Training-Testing protocol was used. This ANN was able, in the Testing phase, to classify approximately 95% of the sample with accuracy. A special sensitivity tool selected only 47 among the 112 independent variables as necessary to train the ANN. In the second step, different types of ANN were tested on the new 47 variables to decide which kind of ANN was better able to classify the sample. This b...Continue Reading

References

Jan 1, 1977·The Journal of Laryngology and Otology·L P Gray
Mar 28, 1998·Substance Use & Misuse·M Buscema
Jan 1, 1992·IEEE Transactions on Neural Networks·S G Kong, B Kosko

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Citations

Apr 9, 2008·Substance Use & Misuse·Lisa DierkerUNKNOWN Tobacco Etiology Research Network
May 18, 2010·Substance Use & Misuse·Heather W MurrayMichael W Otto
Apr 11, 2006·Substance Use & Misuse·Kimberly C BrouwerSteffanie A Strathdee
Jun 13, 2003·Substance Use & Misuse·Furjen DengLou-Jou Lee

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