Identifying health inequalities using artificial neural networks (WHO data)

Zheng Rong Yang, Robert G. Harrison, Weiping Lu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

A method of applying artificial neural networks to identify global health inequalities is presented. By employing this method to 191 WHO countries with 29 health indicators, health inequalities between wealthy and poor countries were identified. This results from the unique property of robust heteroscedastic probabilistic neural network, heteroscedasticity of distribution. Also this method is able to indicate health promotion directions for countries as well as providing the key indicators for promoting the health directions.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
Pages113-118
Number of pages6
Volume4
Publication statusPublished - 2000
Event2000 International Joint Conference on Neural Networks - Como, Italy
Duration: 24 Jul 200027 Jul 2000

Conference

Conference2000 International Joint Conference on Neural Networks
Abbreviated titleIJCNN 2000
CountryItaly
CityComo
Period24/07/0027/07/00

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  • Cite this

    Yang, Z. R., Harrison, R. G., & Lu, W. (2000). Identifying health inequalities using artificial neural networks (WHO data). In Proceedings of the International Joint Conference on Neural Networks (Vol. 4, pp. 113-118)