|Table of Contents|

A Study on Intelligent Diagnosis of Senile Dementia Based on SVM(PDF)

南京师范大学学报(工程技术版)[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2016年02期
Page:
86-
Research Field:
计算机工程
Publishing date:

Info

Title:
A Study on Intelligent Diagnosis of Senile Dementia Based on SVM
Author(s):
Zhang HuiminHu Tai
School of Computer Science and Technology,Nanjing Normal University,Nanjing 210023,China
Keywords:
support vector machineBP neural networkRBF neural networkdementia disease predictiondata mining
PACS:
TP18
DOI:
10.3969/j.issn.1672-1292.2016.02.014
Abstract:
In order to verify that the support vector machine(SVM)is more suitable for predicting diagnosis based on the data of blood routine examination of Alzheimer’s disease,through the simulation experiment,BP neural network,RBF neural network,SVM support vector machine(SVM)are applied to predict the diagnosis of Alzheimer’s disease. Three diagnostic models are established,and the prediction results of the three models are analyzed and compared. The simulation experiments are carried out on the platform of Matlab software,the results show that compared with BP,RBF neural network method,SVM model with high predictive accuracy,short modeling time,good overall performance is more suitable for prediction diagnosis based on the data of blood routine examination of Alzheimer’s disease. This conclusion can be used as a theoretical guide in the practical application.

References:

[1] 贾伟华,马颖,陈若陵,等. 中国部分城乡社区老年痴呆症患病率及其影响因素研究[J]. 安徽医科大学学报,2012,47(8):944-947.
JIA W H,MA Y,CHEN R L,et al. The prevalence of Alzheimer’s disease and its influencing factors in some urban and rural communities in China[J]. J Anhui Med Univ,2012,47(8):944-947. (in Chinese)
[2] 吴信东,叶明全,胡东辉,等. 普适医疗信息管理与服务的关键技术与挑战[J]. 计算机学报,2012,35(5):1-19.
WU X D,YE M Q,HU D H,et al. The key techniques and challenges of pervasive healthcare information management and service[J]. Chinese journal of computers,2012,35(5):1-19. (in Chinese)
[3] 张会敏,叶明全,孟婷玮,等. 遗传算法优化BP神经网络的老年痴呆症智能诊断[J]. 中国数字医学,2014,9(7):81-84.
ZHANG H M,YE M Q,MENG T W,et al. Genetic algorithm-based optimized BP neural network for intelligent diagnostics of dementia disease[J]. Chinese digital medicine,2014,9(7):81-84. (in Chinese)
[4] 郝涛,张智. 基于BP神经网络的原发性肝癌CT图像纹理分析[J]. 中国数字医学,2013,8(8):73-76.
HAO T,ZHANG Z. Texture analysis of CT images of primary liver cancer based on BP neural network[J]. Chinese digital medicine,2013,8(8):73-76. (in Chinese)
[5] JIE B,ZHANG D,BO C,et al. Manifold regularized multitask feature learning for multimodality disease classification[J]. Human brain mapping,2015,36(2):489-507.
[6] MANHUA L,DAOQIANG Z,DINGGANG S. Ensemble sparse classification of Alzheimer’s disease[J]. Neuroimage,2012,60(2):1 106-1 116.
[7] 韩敏. 人工神经网络基础[M]. 大连:大连理工大学出版社,2014.
HAN M. Artificial neural network[M]. Dalian:Dalian University of Technology Press,2014. (in Chinese)
[8] 马锐. 人工神经网络原理[M]. 北京:机械工业出版社,2010.
MA R. The principle of artificial neural network[M]. Beijing:China Machine Press,2010. (in Chinese)
[9] 刘冰,郭海霞. MATLAB神经网络超级学习手册[M]. 北京:人民邮电出版社,2014.
LIU B,GUO H X. MATLAB neural network super learning handbook[M]. Beijing:The Posts and Telecommunications Press,2014. (in Chinese)
[10] 徐琎,王忆勤,邓峰,等. 基于SVM的中医心系证候分类研究[J]. 世界科学技术:中医药现代化,?2010,?12(5):713-717.
XU J,WANG Y Q,DENG F,et al. The heart of TCM syndrome classification research based on SVM[J]. World science and technology:modernization of traditional Chinese medicine,2010,12(5):713-717. (in Chinese)
[11] 张会敏,叶明全,罗永钱,等. 基于RBF神经网络的老年痴呆症智能诊断研究[J]. 中国数字医学,2015,10(6):38-41.
ZHANG H M,YE M Q,LUO Y Q,et al. A study on intelligent diagnosis of senile dementia based on RBF neural network[J]. Chinese digital medicine,2015,10(6):38-41. (in Chinese)
[12] 陈守平,董瑞,罗晓莉. MATLAB神经网络30个案例分析[M]. 北京:北京航空航天大学出版社,2010.
CHEN S P,DONG R,LUO X L. 30 case analysis of MATLAB neural network[M]. Beijing:Beihang University Press,2010. (in Chinese)
[13] 钟志芳. MATLAB神经网络设计与应用[M]. 北京:清华大学出版社,2013.
ZHONG Z F. Design and application of MATLAB neural network[M]. Beijing:Tsinghua University Press,2013. (in Chinese)

Memo

Memo:
-
Last Update: 2016-06-30