Learning Vector Quantization Neural Networks to Medical Diagnosis Problems Research

• Main Authors: Pan Hsin-Li, 潘信利
• Other Authors: Huang Chien-Yu
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/38440289258863126419

碩士 === 樹德科技大學 === 資訊管理研究所 === 96 === Hospital information management has been computerized gradually, and the medical databases are now quite popular in contrast with traditional storage methods. The traditional manual method is not applicable for a large number of information processing. Moreover, medical diagnosis can only rely on past experience of physicians and there are many diversified factors of disease. In this research, the aim is to provide forecast and classification technology by using Artificial Neural Network in order to support the doctors to improve diagnosis with high accuracy. In accordance with this aim, the research method is to address data set of Medical network to classification issues by using Learning Vector Quantization （LVQ） to establish the prediction and classification parameters as well as pre-operating it to choose the meaningful attributes. Furthermore, Taguchi Experimental Design Method （TEDM） approach is also used to adjust the LVQ core parameters in order to obtain the better classification rate and efficient computing processes. This method applied for Medical database with TEDM to find out the ideal parameter portfolio. The experimental results show that, a variety of disease classification, the accurate rate is more than 90 per cent. Moreover, the ideal parameters portfolio found by using TEDM can reduce the number of repeated testing experiment as well as efficiently applied to other disease classification.