Maurelli G, Di Giulio M
Semeion Research Center, Rome, Italy.
Subst Use Misuse. 1998 Feb;33(3):693-708. doi: 10.3109/10826089809115891.
This article makes a comparison among three types of evaluation systems based on a set of data composed of "heavy" alcoholics and "light" alcoholics. The three systems are: 1) a system based on genetic algorithms called BEAGLE; b) seven different types of Artificial Neural Networks; c) a metasystem called MetaNet. The technical aim was to compare the classification capability of these three systems in terms of two classes ("heavy" alcoholics and "light" alcoholics). From the results obtained, the MetaNet system stand out. Globally, it has the best result, followed by the two Artificial Neural Networks, Squash and Logicon Projection. The results obtained prove that the advanced elaboration data systems applied in the social and health fields can be employed in prevention programs having an aim to reduce the social impact of certain pathologies correlated with different kinds of dependence.
本文基于一组由“重度”酗酒者和“轻度”酗酒者组成的数据，对三种评估系统进行了比较。这三种系统分别是：1）一种基于遗传算法的系统，称为BEAGLE；b）七种不同类型的人工神经网络；c）一种元系统，称为MetaNet。技术目标是从两类（“重度”酗酒者和“轻度”酗酒者）的角度比较这三种系统的分类能力。从获得的结果来看，MetaNet系统表现突出。总体而言，它取得了最佳结果，其次是两个人工神经网络，即Squash和Logicon Projection。所获得的结果证明，应用于社会和健康领域的先进数据处理系统可用于预防项目，旨在减少与不同类型成瘾相关的某些病症的社会影响。
