MS Based Nonlinear Methods for Gastric Cancer Early Detection

摘要：

The mortality rate of gastric cancer (GC) ranks the 2nd among all types of cancers. The earlier it is diagnosed, the better its curative effect becomes. As a powerful analyzing technique, SELDI-TOF serves as a new approach for Gastric Mass Spectrometry (GMS) based GC early detection. This article has developed a set of nonlinear approaches for GMS to differentiate the normal persons from the GC suffersthe adapted box dimension calculation method and the clustering featured data mining method. Comparing with other popular SELDI-TOF process techniques, such as SVM, neural networks, RPS, etc, their individual particularities and perfect performance in nonlinear problem analysis, especially after featured respective working mechanism adaptation, credible outcome is well expected.

关键词： Mass Spectrum Gastric Cancer Nonlinear methods Data mining Fractal dimension

作者: Jun Meng Xiangyin Liu Fuming Qiu Jian Huang

作者单位: School of Electrical Engineering, Zhejiang University, Hangzhou, China, 310027 Cancer Institute, The Second Hospital Affiliated to Medical College of Zhejiang University,Hangzhou,

会议类型: 国际会议

会议名称: International Conference on Life System Modeling and Simulation,and International Conference on Intelligent Computing for Sustainable Energy and Environment(2010生命系统建模与仿真国际会议暨m2010可持续能源与环境智能计算国际会议)

会议地点: 无锡

会议语种:英文

页码: 189-195

在线出版日期: 2010-09-17（万方平台首次上网日期，不代表论文的发表时间）