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dc.contributor.authorChen, Yutingen
dc.contributor.authorEr, Meng Jooen
dc.identifier.citationChen, 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.en
dc.description.abstractEvery 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.en
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen
dc.titleBiomedical diagnosis and prediction using parsimonious fuzzy neural networksen
dc.typeConference Paperen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen
dc.contributor.conferenceAnnual Conference on IEEE Industrial Electronics Society (38th : 2012 : Montreal, Canada)en
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