Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/101264
Title: Biomedical diagnosis and prediction using parsimonious fuzzy neural networks
Authors: Chen, Yuting
Er, Meng Joo
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2012
Source: 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.
Abstract: 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.
URI: https://hdl.handle.net/10356/101264
http://hdl.handle.net/10220/16314
DOI: 10.1109/IECON.2012.6388524
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Conference Papers

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