Clinical Decision Support System for Fetal Delivery Using Artificial Neural Network

In the present work an attempt is made to develop a clinical decision support system (CDSS) using the pathological attributes to predict the fetal delivery to be done normal or by surgical procedure. The pathological tests like blood sugar (BR), blood pressure (BP), resistivity index (RI) and systolic-diastolic (S/P) ratio will be recorded at the time of delivery. All attributes lie within a specific range for normal patient. The database consists of the attributes for cases i.e. normal and surgical delivery. Soft computing technique namely artificial neural networks are used for simulator. The attributes from dataset are used for training & testing of ANN models. Three models of ANN are trained using backpropagation algorithm (BPA), radial basis function network (RBFN) and learning vector quantization network (LVQN). The designing factors have been changed to get the optimized model, which gives highest recognition score. The optimized models of BPA, RBFN, and LVQN gave accuracies of 93.75, 99.00, and 87.5 % respectively. Thus RBFN is the best network for mentioned problem. This system will assist doctor to take decision at the critical time of fetal delivery.