An Evolutionary Technique for Medical Diagnostic Risk Factors Selection Dimitrios Mantzaris 1 , George Anastassopoulos 1, 2 , Lazaros Iliadis 2, 3 , Adam Adamopoulos 2, 4 1 Medical Informatics Laboratory, Democritus University of Thrace, GR-68100, Alexandroupolis, Hellas dmantzar@med.duth.gr anasta@med.duth.gr 2 Hellenic Open University, GR-26222, Patras, Greece 3 Department of Forestry & Management of the Environment and Natural Resources, Democritus University of Thrace, GR-68200, Orestiada, Hellas liliadis@fmenr.duth.gr 4 Medical Physics Laboratory, Democritus University of Thrace, GR-68100, Alexandroupolis, Hellas adam@med.duth.gr Abstract This study proposes an Artificial Neural Network (ANN) and Genetic Algorithm model for diagnostic risk factors selection in medicine. A medical dis- ease prediction may be viewed as a pattern classification problem based on a set of clinical and laboratory parameters. Probabilistic Neural Networks (PNNs) were used to face a medical disease prediction. Genetic Algorithm (GA) was used for pruning the PNN. The implemented GA searched for optimal subset of factors that fed the PNN to minimize the number of neurons in the ANN input layer and the Mean Square Error (MSE) of the trained ANN at the testing phase. Moreover, the available data was processed with Receiver Operating Characteristic (ROC) analysis to assess the contribution of each factor to medical diagnosis prediction. The obtained results of the proposed model are in accordance with the ROC analysis, so a number of diagnostic factors in patient’s record can be omitted, without any loss in clinical assessment validity. 1 Introduction Artificial Intelligence (AI) engineering is a relatively modern scientific field and has been reinforced by computer technology advancement. Artificial Neural Net- Please use the following format when citing this chapter: Mantzaris, D., Anastassopoulos, G., Iliadis, L. and Adamopoulos, A., 2009, in IFIP International Federation for Information Processing, Volume 296; Artificial Intelligence Applications and Innovations III; Eds. Iliadis, L., Vlahavas, I., Bramer, M.; (Boston: Springer), pp. 195–203.