DocumentCode :
3326392
Title :
Neural Network application in diagnosis of patient: A case study
Author :
Gharehchopogh, F.S. ; Khalifelu, Z.A.
Author_Institution :
Comput. Eng. Dept., Hecettepe Univ., Turkey
fYear :
2011
fDate :
11-13 July 2011
Firstpage :
245
Lastpage :
249
Abstract :
Patient hep is keys to successfully managing heart diseases and to helping the patients to maintain quality of life. In recent years, with data mining techniques has been extracted important hidden information through clinical data, so these methods confect beneficial information for medical research and health centers. So it will be making a comer designs using hospital information is required fold add influence of clinical centers and hospitals. This paper suppose a decision support model able to help a physician as well as a health care system manage this heart failure population and describes the work on medical data mining and gives useful information about data mining. Finally, the case study is modelled by Neural Network (NN). Training data collects of 40 patients clinical records in health center search about heart problems in persons. The results show that our NN model generates correctly predictions for 85% of test cases.
Keywords :
cardiology; data mining; decision support systems; diseases; health care; information retrieval; medical diagnostic computing; neural nets; patient diagnosis; NN model; decision support model; health care system; health centers; heart disease management; heart failure population; information extraction; medical data mining; medical research; neural network; patient diagnosis; patient hep; quality of life; Bioinformatics; Distributed databases; Europe; Genomics; Medical diagnostic imaging; Medical services; Predictive models; Data Mining; Health Care; Heart Failure; Neural Network;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Networks and Information Technology (ICCNIT), 2011 International Conference on
Conference_Location :
Abbottabad
ISSN :
2223-6317
Print_ISBN :
978-1-61284-940-9
Type :
conf
DOI :
10.1109/ICCNIT.2011.6020937
Filename :
6020937
Link To Document :
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