Bio-Inspired Algorithms for Medical Data Analysis

Abstract

Medical data mining has great potential for exploring the hidden patterns in the data sets of the medical domain. These patterns can be utilized for clinical diagnosis. Bio-inspired algorithms is a new field of research. Its main advantage is knitting together subfields related to the topics of connectionism, social behavior, and emergence. Briefly put, it is the use of computers to model living phenomena and simultaneously the study of life to improve the usage of computers. In this chapter, the authors present an application of four bio-inspired algorithms and meta heuristics for classification of seven different real medical data sets. Two of these algorithms are based on similarity calculation between training and test data while the other two are based on random generation of population to construct classification rules. The results showed a very good efficiency of bio-inspired algorithms for supervised classification of medical data.