[1]张会敏,胡 太.基于SVM的老年痴呆症智能诊断研究[J].南京师范大学学报(工程技术版),2016,16(02):086.[doi:10.3969/j.issn.1672-1292.2016.02.014]
 Zhang Huimin,Hu Tai.A Study on Intelligent Diagnosis of Senile Dementia Based on SVM[J].Journal of Nanjing Normal University(Engineering and Technology),2016,16(02):086.[doi:10.3969/j.issn.1672-1292.2016.02.014]
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基于SVM的老年痴呆症智能诊断研究
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南京师范大学学报(工程技术版)[ISSN:1006-6977/CN:61-1281/TN]

卷:
16卷
期数:
2016年02期
页码:
086
栏目:
计算机工程
出版日期:
2016-06-30

文章信息/Info

Title:
A Study on Intelligent Diagnosis of Senile Dementia Based on SVM
作者:
张会敏胡 太
南京师范大学计算机科学与技术学院,江苏 南京 210023
Author(s):
Zhang HuiminHu Tai
School of Computer Science and Technology,Nanjing Normal University,Nanjing 210023,China
关键词:
支持向量机BP神经网络RBF神经网络老年痴呆症预测数据挖掘
Keywords:
support vector machineBP neural networkRBF neural networkdementia disease predictiondata mining
分类号:
TP18
DOI:
10.3969/j.issn.1672-1292.2016.02.014
文献标志码:
A
摘要:
为了验证支持向量机(SVM)更适用于基于血常规数据的老年痴呆症的预测诊断,通过仿真实验,将BP神经网络、RBF神经网络、SVM支持向量机分别应用于老年痴呆症的预测诊断,建立3种算法对应的诊断模型,并对3种模型的预测结果进行分析比较,仿真实验在Matlab软件平台上进行. 结果表明,与BP、RBF神经网络方法相比,SVM模型预测准确度高,建模时间短,整体性能好,更适用于基于血常规数据的老年痴呆症预测诊断,实际应用时可以此结论作为理论指导.
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.

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备注/Memo

备注/Memo:
收稿日期:2016-03-25. 
基金项目:2013年国家级大学生创新训练项目(201310368027)、2013年省级大学生创新训练项目(AH201310368027). 
通讯联系人:张会敏,硕士研究生,研究方向:数据库与数据挖掘. E-mail:1403451539@qq.com
更新日期/Last Update: 2016-06-30