Result: Market segmentation of organ donors in Egypt: a bio-inspired computational intelligence approach

Title:
Market segmentation of organ donors in Egypt: a bio-inspired computational intelligence approach
Authors:
MOSTAFA, Mohamed M, DURAN TOKSARI, M
Source:
Neural computing & applications (Print). 20(8):1229-1247
Publisher Information:
London: Springer, 2011.
Publication Year:
2011
Physical Description:
print, 122 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Neurology, Neurologie, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Analyse mathématique, Mathematical analysis, Calcul des variations et contrôle optimal, Calculus of variations and optimal control, Probabilités et statistiques, Probability and statistics, Statistiques, Statistics, Inférence à partir de processus stochastiques; analyse des séries temporelles, Inference from stochastic processes; time series analysis, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Méthodes numériques en programmation mathématique, optimisation et calcul variationnel, Numerical methods in mathematical programming, optimization and calculus of variations, Optimisation et calcul variationnel numériques, Numerical methods in optimization and calculus of variations, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Intelligence artificielle, Artificial intelligence, Apprentissage et systèmes adaptatifs, Learning and adaptive systems, Analyse discriminante, Discriminant analysis, Análisis discriminante, Analyse statistique, Statistical analysis, Análisis estadístico, Calcul neuronal, Neural computation, computación neuronal, Classification automatique (statistiques), Cluster analysis (statistics), Comportement, Behavior, Conducta, Connaissance, Knowledge, Conocimiento, Fonction base radiale, Radial basis function, Función radial base, Intelligence, Inteligencia, Méthode analyse, Analysis method, Método análisis, Méthode optimisation, Optimization method, Método optimización, Méthode statistique, Statistical method, Método estadístico, Optimisation, Optimization, Optimización, Réseau neuronal, Neural network, Red neuronal, Segmentation, Segmentación, Système apprentissage, Learning systems, 49XX, 62H30, 62M45, 65K10, 65Kxx, Application auto organisante, Apprentissage machine, Clustering, Réseau Kohonen, SOM, SVM
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
Gulf University for Science and Technology, Kuwait, Kuwait Erciyes University, Kayseri, Turkey
ISSN:
0941-0643
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=24725615
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems

Mathematics
Accession Number:
edscal.24725615
Database:
PASCAL Archive

Further Information

First performed in 1954, organ transplantation is a universally practiced clinical procedure. This study uses ant colony optimization (ACO), radial basis function neural network (RBFNN), Kohonen's self-organizing maps (SOM), and support vector machines (SVMs) to examine the effect of various cognitive, psychographic, and attitudinal factors on organ donation. ACO, RBFNN, SOM, and SVMs are compared to a standard statistical method (linear discriminant analysis [LDA]). The variable sets considered are altruistic values, perceived risks/benefits, knowledge, attitudes toward organ donation, and intention to donate organs. The paper shows how it is possible to identify various dimensions of organ donation behavior by uncovering complex patterns in the dataset and also shows the classification and clustering abilities of machine-learning systems.