Healthcare professionals usually should make complex decisions
with far reaching consequences and associated risks in health
care fields. As it was demonstrated in other industries, the ability
to drill down into pertinent data to explore knowledge behind the
data can greatly facilitate superior, informed decisions to ensue
the facts. Nature has always inspired researchers to develop
models of solving the problems. Bee colony algorithm (BCA),
based on the self-organized behavior of social insects is one of
the most popular member of the family of population oriented,
nature inspired meta-heuristic swarm intelligence method
which has been proved its superiority over some other nature
inspired algorithms. The objective of this model was to identify
valid novel, potentially useful, and understandable correlations
and patterns in existing data. This review employs a thematic
analysis of online series of academic papers to outline BCA in
medical hive, reducing the response and computational time and
optimizing the problems. To illustrate the benefits of this model,
the cases of disease diagnose system are presented