Biomedical diagnosis and prediction using parsimonious fuzzy neural networks
Every doctor needs to learn how to diagnose accurately and reliably. Based on observations and knowledge, they have to diagnose illnesses and give individual treatment to each patient. Although there are numerous medical books, records and courses assisting doctors with their deduction, the medical...
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sg-ntu-dr.10356-1012642020-03-07T13:24:50Z Biomedical diagnosis and prediction using parsimonious fuzzy neural networks Chen, Yuting Er, Meng Joo School of Electrical and Electronic Engineering Annual Conference on IEEE Industrial Electronics Society (38th : 2012 : Montreal, Canada) DRNTU::Engineering::Electrical and electronic engineering Every doctor needs to learn how to diagnose accurately and reliably. Based on observations and knowledge, they have to diagnose illnesses and give individual treatment to each patient. Although there are numerous medical books, records and courses assisting doctors with their deduction, the medical knowledge outdates quickly and cannot replace one's own experience. To handle this challenge, this paper applies the fast and accurate online self-organizing scheme for parsimonious fuzzy neural networks (FAOS-PFNN) to biomedical diagnosis and prediction. Unlike other fuzzy neural networks, the FAOS-PFNN is a more practical method which does not require structure identification in advance and can achieve a more compact network structure. The effectiveness of the FAOS-PFNN has been tested on diagnosis of breast cancer and prediction of Parkinson's Disease respectively. Simulation studies demonstrate that the FAOS-PFNN algorithm can efficiently and accurately diagnose and therefore improve the computer assisted medical diagnosis. 2013-10-10T01:25:34Z 2019-12-06T20:35:48Z 2013-10-10T01:25:34Z 2019-12-06T20:35:48Z 2012 2012 Conference Paper Chen, Y., & Er, M. J. (2012). Biomedical diagnosis and prediction using parsimonious fuzzy neural networks. IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society, pp.1477-1482. https://hdl.handle.net/10356/101264 http://hdl.handle.net/10220/16314 10.1109/IECON.2012.6388524 en |
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DRNTU::Engineering::Electrical and electronic engineering Chen, Yuting Er, Meng Joo Biomedical diagnosis and prediction using parsimonious fuzzy neural networks |
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Every doctor needs to learn how to diagnose accurately and reliably. Based on observations and knowledge, they have to diagnose illnesses and give individual treatment to each patient. Although there are numerous medical books, records and courses assisting doctors with their deduction, the medical knowledge outdates quickly and cannot replace one's own experience. To handle this challenge, this paper applies the fast and accurate online self-organizing scheme for parsimonious fuzzy neural networks (FAOS-PFNN) to biomedical diagnosis and prediction. Unlike other fuzzy neural networks, the FAOS-PFNN is a more practical method which does not require structure identification in advance and can achieve a more compact network structure. The effectiveness of the FAOS-PFNN has been tested on diagnosis of breast cancer and prediction of Parkinson's Disease respectively. Simulation studies demonstrate that the FAOS-PFNN algorithm can efficiently and accurately diagnose and therefore improve the computer assisted medical diagnosis. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Chen, Yuting Er, Meng Joo |
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Conference or Workshop Item |
author |
Chen, Yuting Er, Meng Joo |
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Chen, Yuting |
title |
Biomedical diagnosis and prediction using parsimonious fuzzy neural networks |
title_short |
Biomedical diagnosis and prediction using parsimonious fuzzy neural networks |
title_full |
Biomedical diagnosis and prediction using parsimonious fuzzy neural networks |
title_fullStr |
Biomedical diagnosis and prediction using parsimonious fuzzy neural networks |
title_full_unstemmed |
Biomedical diagnosis and prediction using parsimonious fuzzy neural networks |
title_sort |
biomedical diagnosis and prediction using parsimonious fuzzy neural networks |
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2013 |
url |
https://hdl.handle.net/10356/101264 http://hdl.handle.net/10220/16314 |
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