A non-symbolic implementation of abdominal pain estimation in childhood

The abdominal pain is a very common disease in childhood, which lurks complications. Pediatric surgeons have to estimate at least 15 clinical and laboratory factors in order to make a diagnosis and decide about performing a surgical operation of the abdomen. Artificial Neural Networks (ANNs) are particular implementations of Artificial Intelligence (AI) systems and they are used in a wide area of application fields. This study examines the implementation of ANN architectures, using Multi-Layer Perceptron (MLP) neural networks and Probabilistic Neural Networks (PNN) architectures, in order to specify the appropriate ANN structure for abdominal pain estimation in childhood. The architecture with the best performance is a fully interconnected MLP neural network with an input layer of 15 nodes, one hidden layer of 5 neurons and an output layer, with error back-propagation algorithm being used as the learning scheme. In the output layer, the estimation of appendicitis’ stage is reached automatically. The proposed ANN achieved a percentage of 88.5% of correct classification on testing set cases. Further analysis of obtained results, exhibited the ability of ANN for distinguishing the necessity of a case for operative treatment of abdominal pain based on diagnostic features, attaining a percentage of 100% of successful prognosis over the cases of testing set. The aim of proposed MLP neural network is to assist surgeons in appendicitis prediction, avoiding an unnecessary operative treatment.