Summary: In Lecture Notes in Artificial Intelligence 1531- PRICAI'98: Topics in Artificial
Intelligence, H. Lee & H. Motoda (Eds.). Berlin:Springer Verlag.
Applying Knowledge Discovery to Predict Infectious
Syed Sibte Raza Abidi and Alwyn Goh
School of Computer Sciences
Universiti Sains Malaysia
11800 Penang, Malaysia.
Abstract. Predictive modelling, in a knowledge discovery context, is regarded
as the problem of deriving predictive knowledge from historical/temporal data.
Here we argue that neural networks, an established computational technology,
can efficaciously be used to perform predictive modelling, i.e. to explore the
intrinsic dynamics of temporal data. Infectious-disease epidemic risk
management is a candidate area for exploiting the potential of neural network
based predictive modelling--the idea is to model time series derived from
bacteria-antibiotic sensitivity and resistivity patterns as it is believed that
bacterial sensitivity and resistivity to any antibiotic tends to undergo temporal
fluctuations. The objective of epidemic risk management is to obtain forecasted
values for the bacteria-antibiotic sensitivity and resistivity profiles, which could