Medical disease prediction using Grey Wolf optimization and auto encoder based recurrent neural network

Abstract

Big data development in biomedical and medical service networks provides a research on medical data benefits, early ailment detection, patient care and network administrations.e-Health applications are particularly important for the patients who are unfit to see a specialist or any health expert. The objective is to encourage clinicians and families to predict disease using Machine Learning (ML) procedures. In addition, diverse regions show important qualities of certain provincial ailments, which may hinder the forecast of disease outbreaks. The objective of this work is to predict the different kinds of diseases using Grey Wolf optimization and auto encoder based Recurrent Neural Network (GWO+RNN). The features are selected using GWO and the diseases are predicted by using RNN method. Initially the GWO algorithm avoids the irrelevant and redundant attributes significantly, after the features are forwarded to the RNN classifier. The experimental result proved that the performance of GWO+RNN algorithm achieved better than existing method like Group Search Optimizer and Fuzzy Min-Max Neural Network (GFMMNN) approach. The GWO-RNN method used the medical UCI database based on various datasets such as Hungarian, Cleveland, PID, mammographic masses, Switzerland and performance was measured with the help of efficient metrics like accuracy, sensitivity and specificity. The proposed GWO+RNN method achieved 16.82% of improved prediction accuracy for Cleveland dataset.