Implementation of fuzzy associative memory toward optimizing a neural network model to predict total iron binding capacity

Abstract

ObjectivesDetermining the optimal number of hidden layer neurons in a neural network (NN) is an important issue. In this research, a fuzzy associative memory (FAM) model will be developed to determine the hidden neuron of neural network architecture. MethodsThe material for this research is data from chronic kidney disease (CKD) patients on hemodialysis to predict total iron binding capacity (TIBC). The research begins by choosing the most appropriate backpropagation algorithm. The FAM system was developed using the product-correlation coding operator, the product-max composition relation, and a combination of winner-take-all and weighted average. Testing is done by calculating the average error (MSE) of training and testing, as well as the average error for gradients. ResultsWe selected 12 alternative network architectures. The Levenberg-Marquardt backpropagation algorithm was chosen for its best performance. In testing the training data, the average percentage of errors was 7.16% for MSE and 12.93% for gradients. The test results of the FAM system show an average error of 0%. The test results on the test data show an average error for MSE of 7.67% and an average error for gradients of 8.55%. ConclusionBesides being able to predict the MSE and gradient for a particular NN architecture, FAM can also determine the NN architectural model that provides the optimal combination of MSE and gradient. Further research will improve the performance of the FAM system.