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

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Abstract

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.

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  1. Gulf University for Science and Technology, Kuwait, Kuwait

    Mohamed M. Mostafa

  2. Erciyes University, Kayseri, Turkey

    M. Duran Toksari

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  1. Mohamed M. Mostafa
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Correspondence to Mohamed M. Mostafa.

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Appendix: SVM implemented in the R environment

Appendix: SVM implemented in the R environment

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Mostafa, M.M., Toksari, M.D. Market segmentation of organ donors in Egypt: a bio-inspired computational intelligence approach. Neural Comput & Applic 20, 1229–1247 (2011). https://doi.org/10.1007/s00521-011-0552-3

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