International Journal of Engineering and Technology

Neural Network(NN) is widely used in medical research because of its cost effectiveness and easy-to-use systems. NN plays an important role in the decision support system. The sole reason to use the efficient NN model is to manage the rapidly increasing medical data effectively and use these data points for accurate prediction of diseases and thereby providing better health care to the patients. In this paper we have made use of two classification algorithmsi.e. feedforwardbackpropagation neural network(FFBPNN) and Cascade Correlationfeedforward network (CCFFN) and compared the performance of both the algorithms on the basis of sensitivity, specificity and accuracy. The experiment is done on Bupa liver dataset using FFBPNN and CCFFN model using different set of neurons which are trained with Levenberg-Marquardt training algorithm(trainlm) and ResilientBackpropagationtraining algorithm (trainrp). This paper has provided the comparative analysis among different classifiers using two different training algorithmsto find the benign and malignant patients.