Identifying health inequalities using artificial neural networks (WHO data)

Zheng Rong Yang; Robert G. Harrison; Weiping Lu

Identifying health inequalities using artificial neural networks (WHO data)

Zheng Rong Yang, Robert G. Harrison, Weiping Lu

School of Engineering & Physical Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Proceedings of the International Joint Conference on Neural Networks
Pages	113-118
Number of pages	6
Volume	4
Publication status	Published - 2000
Event	2000 International Joint Conference on Neural Networks - Como, Italy Duration: 24 Jul 2000 → 27 Jul 2000

Conference

Conference	2000 International Joint Conference on Neural Networks
Abbreviated title	IJCNN 2000
Country/Territory	Italy
City	Como
Period	24/07/00 → 27/07/00

Cite this

@inproceedings{22058e445c364df6ac2f81f17fd5c28b,

title = "Identifying health inequalities using artificial neural networks (WHO data)",

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.",

author = "Yang, {Zheng Rong} and Harrison, {Robert G.} and Weiping Lu",

year = "2000",

language = "English",

volume = "4",

pages = "113--118",

booktitle = "Proceedings of the International Joint Conference on Neural Networks",

note = "2000 International Joint Conference on Neural Networks, IJCNN 2000 ; Conference date: 24-07-2000 Through 27-07-2000",

}

TY - GEN

T1 - Identifying health inequalities using artificial neural networks (WHO data)

AU - Yang, Zheng Rong

AU - Harrison, Robert G.

AU - Lu, Weiping

PY - 2000

Y1 - 2000

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0033717751&partnerID=8YFLogxK

M3 - Conference contribution

VL - 4

SP - 113

EP - 118

BT - Proceedings of the International Joint Conference on Neural Networks

T2 - 2000 International Joint Conference on Neural Networks

Y2 - 24 July 2000 through 27 July 2000

ER -

Identifying health inequalities using artificial neural networks (WHO data)

Abstract

Conference

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