Recurrent Neural Network Architectures for Analysing Biomedical Data Sets

Abstract

This paper presents the utilisation of dynamical recurrent neural network architectures in the purpose of classifying the Sickle Cell disorder data. It is indicted that recurrent neural networks such as the Jordan network produce a great improvement with clinical data sets and have helped in acquiring high accuracy. The main aim of this study is to provide a sophisticated model to differentiate applications of dynamical neural networks for medically related problems. We attempt to classify the amount of medications for each patient with Sickle Cell disorder. We use different recurrent neural network architectures in terms of examining performance for each model within this study. The motivation for the classification approach used in this study is to support medical sectors to offer proper therapy advice depending on the former data set. The outcomes yield from different classifiers during our experiments indicated that Elman and hybrid recurrent neural networks produced inferior results when compared to Jordan neural networks. Results have indicated that for the recurrent network models tested, the Jordan architecture was found to yield considerably better results over the range of performance measures that been selected for this research.