Biomedical diagnosis and prediction using parsimonious fuzzy neural networks

• Published in: IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society pp. 1477 - 1482
• Main Authors: Yuting Chen, Er Meng Joo
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2012
• Subjects: Biomedical diagnosis and prediction; Biomedical measurements; Clustering algorithms; fast and accurate online self-organizing scheme for parsimonious fuzzy neural networks (FAOS-PFNN); fuzzy neural networks (FNN); Knowledge engineering; Organizing; Prediction algorithms; Reliability; Training
• ISBN: 9781467324199; 1467324191
• ISSN: 1553-572X
• DOI: 10.1109/IECON.2012.6388524

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.