title: How can bee colony algorithm serve medicine?
abstract: 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.
authors.affiliation: Department of Information Technology, Bushehr University of Medical Sciences, Bushehr, Iran;
authors.forename: Zeinab
authors.initials: Z
authors.lastname: Salehahmadi
authors.affiliation: Shahid Beheshti University of Medical Sciences, Tehran, Iran.
authors.forename: Amir
authors.initials: A
authors.lastname: Manafi
keywords: Bee colony algorithm; Diagnose diseases system; Medicine