Feature Selection for ANNs using Intelligent Genetic Algorithms in Diagnostic of Hepatitis

Artificial Neural Networks (ANNs) can be used successfully to detect the Hepatitis patient from healthy people using statistical estimates of blood factors and signs of the disease as input features. One of the main problems facing the use of ANNs is the selection of the best inputs to the ANN, allowing the creation of compact, highly accurate networks that require comparatively little pre-processing. This paper examines the use of a Genetic Algorithm (GA) to select the most significant input features from a large set of possible features in diagnostic of the Hepatic patients. Using a large set of 19 different features, the GA is able to select a set of 10 features that give 100% recognition accuracy.

کلیدواژه ها:

Artificial Neural Networks ، Genetic Algorithm ، Hepatitis ، MLP