Neural network based hybrid prediction models for healthcare applications

Prediction models based on different concepts have been proposed in recent years. Improving the accuracy of prediction models has remained as a challenging task for researchers. In the development of time series analysis, it is well known that many phenomena are non-linear and hence only linear time...

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Bibliographic Details
Main Author:	Purwanto
Format:	Thesis
Published:	2012
Subjects:	QA75.5-76.95 Electronic computers Computer science
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id	my-mmu-ep.5221
record_format	uketd_dc
spelling	my-mmu-ep.52212014-02-17T01:45:34Z Neural network based hybrid prediction models for healthcare applications 2012-08 Purwanto, QA75.5-76.95 Electronic computers. Computer science Prediction models based on different concepts have been proposed in recent years. Improving the accuracy of prediction models has remained as a challenging task for researchers. In the development of time series analysis, it is well known that many phenomena are non-linear and hence only linear time series prediction models are not sufficient to get accurate prediction. In the real-world, the time series data consist of complex linear and non-linear patterns and it may be difficult to obtain high prediction accuracy rates using only linear or only neural network model. Hybrid model which combines both linear and neural network models provides a better solution for time series prediction. In this study, new hybrid models termed as enhanced hybrid model (EHM), adaptive enhanced hybrid model (AEHM), dual enhanced hybrid model with fuzzy logic (DEHM-F), are proposed for univariate time series prediction. The proposed models take into account the pattern of data in selecting the best linear model and also in optimizing the configuration of the neural network. An enhanced adaptive neuro-fuzzy inference system (E-ANFIS) is also proposed for univariate time series prediction. E-ANFIS makes use of a strategy to determine the optimum number of input lags. 2012-08 Thesis http://shdl.mmu.edu.my/5221/ http://vlib.mmu.edu.my/diglib/login/dlusr/login.php phd doctoral Multimedia University Faculty of Computing & Informatics
institution	Multimedia University
collection	MMU Institutional Repository
topic	QA75.5-76.95 Electronic computers Computer science
spellingShingle	QA75.5-76.95 Electronic computers Computer science Purwanto, Neural network based hybrid prediction models for healthcare applications
description	Prediction models based on different concepts have been proposed in recent years. Improving the accuracy of prediction models has remained as a challenging task for researchers. In the development of time series analysis, it is well known that many phenomena are non-linear and hence only linear time series prediction models are not sufficient to get accurate prediction. In the real-world, the time series data consist of complex linear and non-linear patterns and it may be difficult to obtain high prediction accuracy rates using only linear or only neural network model. Hybrid model which combines both linear and neural network models provides a better solution for time series prediction. In this study, new hybrid models termed as enhanced hybrid model (EHM), adaptive enhanced hybrid model (AEHM), dual enhanced hybrid model with fuzzy logic (DEHM-F), are proposed for univariate time series prediction. The proposed models take into account the pattern of data in selecting the best linear model and also in optimizing the configuration of the neural network. An enhanced adaptive neuro-fuzzy inference system (E-ANFIS) is also proposed for univariate time series prediction. E-ANFIS makes use of a strategy to determine the optimum number of input lags.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Purwanto,
author_facet	Purwanto,
author_sort	Purwanto,
title	Neural network based hybrid prediction models for healthcare applications
title_short	Neural network based hybrid prediction models for healthcare applications
title_full	Neural network based hybrid prediction models for healthcare applications
title_fullStr	Neural network based hybrid prediction models for healthcare applications
title_full_unstemmed	Neural network based hybrid prediction models for healthcare applications
title_sort	neural network based hybrid prediction models for healthcare applications
granting_institution	Multimedia University
granting_department	Faculty of Computing & Informatics
publishDate	2012
_version_	1747829565217046528

Neural network based hybrid prediction models for healthcare applications

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