Predicting Diseases Using Multi-BackPropagation

A Computer-based medical system plays an important role in the current practice of medicine. Initially, computer is used to store and manage information effectively. The computer becomes more important with the introduction of the intelligent system. The intelligent medical system increases the abi...

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Main Author:	Wan Hussain, Wan Ishak
Format:	Thesis
Language:	eng eng
Published:	2002
Subjects:	RC Internal medicine
Online Access:	https://etd.uum.edu.my/979/1/WAN_HUSSAIN_B._WAN_ISHAK.pdf https://etd.uum.edu.my/979/2/1.WAN_HUSSAIN_B._WAN_ISHAK.pdf
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spelling	my-uum-etd.9792013-07-24T12:09:54Z Predicting Diseases Using Multi-BackPropagation 2002 Wan Hussain, Wan Ishak Sekolah Siswazah Graduate School RC Internal medicine A Computer-based medical system plays an important role in the current practice of medicine. Initially, computer is used to store and manage information effectively. The computer becomes more important with the introduction of the intelligent system. The intelligent medical system increases the ability of medical practitioners in providing diagnosis and prognosis. Neural network is one of the artificial intelligence techniques that emulate the human neuron function. Neural network enable the computer to "learn" and "think" like human. However, learning usually involves a large amount of data. If more data is used, the network complexity will be increased. Complex network is hard to learn and take more time to generalize. Thus this study proposed a multi-network approach as oppose to the single network approach. Multi-network approach does not require any changes in neural network learning algorithm. Instead, the large data is divided into several smaller categories or network. Both approaches are tested and compared. The results show that the estimation time for the single network with 26 variables based on 7466 data set is approximately 1,037,472,836 milliseconds to complete the learning with 100 percent generalization performance. On the other hand, based on 256 data sets the network takes 2,459,172,864 milliseconds to complete the learning. The epochs are estimated as 359,544 and 26,2 14,400 respectively. In the multi-network approach, five different networks and one integration network were constructed. The experiments showed that all six networks managed to learn the data completely in only several epochs. The time taken by the networks are 281, 197, 32, 440, 83 and 22 respectively for the risk factor, medication, investigation, ECG, complication and integrating network. On average, this approach takes 175.833 milliseconds and 7.66667 epochs to complete the learning. The total training time for all networks to learn is 1055 milliseconds with 46 epochs. Although many networks have to be constructed and trained separately, the multinetwork approach has reduced the complexity of network with large data set and has overcome the limitation of the single network approach. This is because the networks represent all the possible combination of data, which were all used to train them respectively. That is in the multi network approach all data sets are used in training. The knowledge (weight) produced by the network can be applied for all possible data sets. 2002 Thesis https://etd.uum.edu.my/979/ https://etd.uum.edu.my/979/1/WAN_HUSSAIN_B._WAN_ISHAK.pdf application/pdf eng validuser https://etd.uum.edu.my/979/2/1.WAN_HUSSAIN_B._WAN_ISHAK.pdf application/pdf eng public masters masters Universiti Utara Malaysia
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
topic	RC Internal medicine
spellingShingle	RC Internal medicine Wan Hussain, Wan Ishak Predicting Diseases Using Multi-BackPropagation
description	A Computer-based medical system plays an important role in the current practice of medicine. Initially, computer is used to store and manage information effectively. The computer becomes more important with the introduction of the intelligent system. The intelligent medical system increases the ability of medical practitioners in providing diagnosis and prognosis. Neural network is one of the artificial intelligence techniques that emulate the human neuron function. Neural network enable the computer to "learn" and "think" like human. However, learning usually involves a large amount of data. If more data is used, the network complexity will be increased. Complex network is hard to learn and take more time to generalize. Thus this study proposed a multi-network approach as oppose to the single network approach. Multi-network approach does not require any changes in neural network learning algorithm. Instead, the large data is divided into several smaller categories or network. Both approaches are tested and compared. The results show that the estimation time for the single network with 26 variables based on 7466 data set is approximately 1,037,472,836 milliseconds to complete the learning with 100 percent generalization performance. On the other hand, based on 256 data sets the network takes 2,459,172,864 milliseconds to complete the learning. The epochs are estimated as 359,544 and 26,2 14,400 respectively. In the multi-network approach, five different networks and one integration network were constructed. The experiments showed that all six networks managed to learn the data completely in only several epochs. The time taken by the networks are 281, 197, 32, 440, 83 and 22 respectively for the risk factor, medication, investigation, ECG, complication and integrating network. On average, this approach takes 175.833 milliseconds and 7.66667 epochs to complete the learning. The total training time for all networks to learn is 1055 milliseconds with 46 epochs. Although many networks have to be constructed and trained separately, the multinetwork approach has reduced the complexity of network with large data set and has overcome the limitation of the single network approach. This is because the networks represent all the possible combination of data, which were all used to train them respectively. That is in the multi network approach all data sets are used in training. The knowledge (weight) produced by the network can be applied for all possible data sets.
format	Thesis
qualification_name	masters
qualification_level	Master's degree
author	Wan Hussain, Wan Ishak
author_facet	Wan Hussain, Wan Ishak
author_sort	Wan Hussain, Wan Ishak
title	Predicting Diseases Using Multi-BackPropagation
title_short	Predicting Diseases Using Multi-BackPropagation
title_full	Predicting Diseases Using Multi-BackPropagation
title_fullStr	Predicting Diseases Using Multi-BackPropagation
title_full_unstemmed	Predicting Diseases Using Multi-BackPropagation
title_sort	predicting diseases using multi-backpropagation
granting_institution	Universiti Utara Malaysia
granting_department	Sekolah Siswazah
publishDate	2002
url	https://etd.uum.edu.my/979/1/WAN_HUSSAIN_B._WAN_ISHAK.pdf https://etd.uum.edu.my/979/2/1.WAN_HUSSAIN_B._WAN_ISHAK.pdf
_version_	1747827058865602560

Predicting Diseases Using Multi-BackPropagation

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