HIV data analysis via rule extraction using rough sets

Thando Tettey; Fulufhelo V. Nelwamondo; Tshilidzi Marwala

doi:10.1109/INES.2007.4283681

HIV data analysis via rule extraction using rough sets

Thando Tettey
, Fulufhelo V. Nelwamondo
, Tshilidzi Marwala

University of the Witwatersrand

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

18 Citations (Scopus)

Abstract

The paper presents an analysis HIV data obtained from a survey performed on pregnant women by the Department of Health in South Africa. The HIV data is analysed by formulating a rough set approximation of the six demographic variables analysed. These variables are Race,, Age of Mother, Education, Gravidity, Parity and Age of Father. It is found that of the 4096 possible subsets in the input space, the data only represents 225 of those cases with 130 cases being discernible and 96 cases indiscernible. The rough sets analysis is suggested as a quick way of analysing data and rule extraction over Neuro-fuzzy models when it comes to data driven identification. Comparisons of rule extraction using rough sets and using neuro-fuzzy is conducted and the results are in favour of the rough sets.

Original language	English
Title of host publication	INES 2007 - 11th International Conference on Intelligent Engineering Systems, Proceedings
Pages	105-110
Number of pages	6
DOIs	https://doi.org/10.1109/INES.2007.4283681
Publication status	Published - 2007
Externally published	Yes
Event	INES 2007 - 11th International Conference on Intelligent Engineering Systems - Budapest, Hungary Duration: 29 Jun 2007 → 1 Jul 2007

Publication series

Name	INES 2007 - 11th International Conference on Intelligent Engineering Systems, Proceedings

Conference

Conference	INES 2007 - 11th International Conference on Intelligent Engineering Systems
Country/Territory	Hungary
City	Budapest
Period	29/06/07 → 1/07/07

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 5 Gender Equality

ASJC Scopus subject areas

Artificial Intelligence
Control and Systems Engineering

Access to Document

10.1109/INES.2007.4283681

Cite this

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title = "HIV data analysis via rule extraction using rough sets",

abstract = "The paper presents an analysis HIV data obtained from a survey performed on pregnant women by the Department of Health in South Africa. The HIV data is analysed by formulating a rough set approximation of the six demographic variables analysed. These variables are Race,, Age of Mother, Education, Gravidity, Parity and Age of Father. It is found that of the 4096 possible subsets in the input space, the data only represents 225 of those cases with 130 cases being discernible and 96 cases indiscernible. The rough sets analysis is suggested as a quick way of analysing data and rule extraction over Neuro-fuzzy models when it comes to data driven identification. Comparisons of rule extraction using rough sets and using neuro-fuzzy is conducted and the results are in favour of the rough sets.",

author = "Thando Tettey and Nelwamondo, \{Fulufhelo V.\} and Tshilidzi Marwala",

year = "2007",

doi = "10.1109/INES.2007.4283681",

language = "English",

isbn = "1424411475",

series = "INES 2007 - 11th International Conference on Intelligent Engineering Systems, Proceedings",

pages = "105--110",

booktitle = "INES 2007 - 11th International Conference on Intelligent Engineering Systems, Proceedings",

note = "INES 2007 - 11th International Conference on Intelligent Engineering Systems ; Conference date: 29-06-2007 Through 01-07-2007",

}

Tettey, T, Nelwamondo, FV & Marwala, T 2007, HIV data analysis via rule extraction using rough sets. in INES 2007 - 11th International Conference on Intelligent Engineering Systems, Proceedings., 4283681, INES 2007 - 11th International Conference on Intelligent Engineering Systems, Proceedings, pp. 105-110, INES 2007 - 11th International Conference on Intelligent Engineering Systems, Budapest, Hungary, 29/06/07. https://doi.org/10.1109/INES.2007.4283681

HIV data analysis via rule extraction using rough sets. / Tettey, Thando; Nelwamondo, Fulufhelo V.; Marwala, Tshilidzi.
INES 2007 - 11th International Conference on Intelligent Engineering Systems, Proceedings. 2007. p. 105-110 4283681 (INES 2007 - 11th International Conference on Intelligent Engineering Systems, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - HIV data analysis via rule extraction using rough sets

AU - Tettey, Thando

AU - Nelwamondo, Fulufhelo V.

AU - Marwala, Tshilidzi

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AB - The paper presents an analysis HIV data obtained from a survey performed on pregnant women by the Department of Health in South Africa. The HIV data is analysed by formulating a rough set approximation of the six demographic variables analysed. These variables are Race,, Age of Mother, Education, Gravidity, Parity and Age of Father. It is found that of the 4096 possible subsets in the input space, the data only represents 225 of those cases with 130 cases being discernible and 96 cases indiscernible. The rough sets analysis is suggested as a quick way of analysing data and rule extraction over Neuro-fuzzy models when it comes to data driven identification. Comparisons of rule extraction using rough sets and using neuro-fuzzy is conducted and the results are in favour of the rough sets.

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M3 - Conference contribution

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SN - 1424411475

SN - 9781424411474

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BT - INES 2007 - 11th International Conference on Intelligent Engineering Systems, Proceedings

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Y2 - 29 June 2007 through 1 July 2007

ER -

HIV data analysis via rule extraction using rough sets

Abstract

Publication series

Conference

UN SDGs

ASJC Scopus subject areas

Access to Document

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