  The wealth of computerised medical information becoming readily available
presents the opportunity to examine patterns of illnesses, therapies and
responses. These patterns may be able to predict illnesses that a patient is
likely to develop, allowing the implementation of preventative actions. In this
paper sequential rule mining is applied to a General Practice database to find
rules involving a patients age, gender and medical history. By incorporating
these rules into current health-care a patient can be highlighted as
susceptible to a future illness based on past or current illnesses, gender and
year of birth. This knowledge has the ability to greatly improve health-care
and reduce health-care costs.
