An Evolutionary Technique for Medical Diagnostic Risk Factors Selection

Abstract

This study proposes an Artificial Neural Network (ANN) and Genetic Algorithm model for diagnostic risk factors selection in medicine. A medical disease 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.