Detection of diabetes using genetic programming

Diabetes is one of the common and rapidly increasing diseases in the world. It is a major health problem in most of the countries. Due to its importance, the need for automated detection of this disease is increasing. The method proposed here uses genetic programming (GP) and a variation of genetic programming called GP with comparative partner selection (CPS) for diabetes detection. The proposed system consists of two stages. In first stage we use genetic programming to produce an individual from training data, that converts the available features to a single feature such that it has different values for healthy and patient (diabetes) data. In the next stage we use test data for testing of that individual. The proposed system was able to achieve 78.5±2.2% accuracy. The results showed that GP based classifier can assist in the diagnosis of diabetes disease.