  In this paper, we present a method to optimise rough set partition sizes, to
which rule extraction is performed on HIV data. The genetic algorithm
optimisation technique is used to determine the partition sizes of a rough set
in order to maximise the rough sets prediction accuracy. The proposed method is
tested on a set of demographic properties of individuals obtained from the
South African antenatal survey. Six demographic variables were used in the
analysis, these variables are; race, age of mother, education, gravidity,
parity, and age of father, with the outcome or decision being either HIV
positive or negative. Rough set theory is chosen based on the fact that it is
easy to interpret the extracted rules. The prediction accuracy of equal width
bin partitioning is 57.7% while the accuracy achieved after optimising the
partitions is 72.8%. Several other methods have been used to analyse the HIV
data and their results are stated and compared to that of rough set theory
(RST).
