Title :
HIV Data Analysis via Rule Extraction using Rough Sets
Author :
Tettey, Thando ; Nelwamondo, Fulufhelo V. ; Marwala, Tshilidzi
Author_Institution :
Univ. of the Witwatersrand, Johannesburg
fDate :
June 29 2007-July 2 2007
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.
Keywords :
approximation theory; data analysis; fuzzy neural nets; medical computing; rough set theory; HIV data analysis; South Africa; demographic variables; human immunodeficiency virus; neuro-fuzzy models; rough set approximation; rule extraction; Acquired immune deficiency syndrome; Africa; Data analysis; Data mining; Demography; Government; Human immunodeficiency virus; Neural networks; Pregnancy; Rough sets;
Conference_Titel :
Intelligent Engineering Systems, 2007. INES 2007. 11th International Conference on
Conference_Location :
Budapest
Print_ISBN :
1-4244-1147-5
Electronic_ISBN :
1-4244-1148-3
DOI :
10.1109/INES.2007.4283681
