TY  - CONF
N2  - Clinical Database has enormous quantity of information about patients and their diseases. The database mainly contains clinical consultation details, family history, medical lab report and other information which are considered to taking a final diagnostic decision by physician. Clinical databases are widely utilized by the numerous researchers for predicting different diseases. The current diabetes diagnosis methods are carried out based on the impact of various medical test and the results of physical examination. The new and innovative prediction methods are projected in this research to identify the diabetic disease, its types and complications from the clinical database in an efficiently and an economically faster manner. © 2014 IEEE.
ID  - scholars4246
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938790845&doi=10.1109%2fICCOINS.2014.6868414&partnerID=40&md5=15d3a467507e96430feedde1d43f0b81
KW  - Clinical research; Cluster analysis; Clustering algorithms; Database systems; Diagnosis; Forecasting; Fuzzy logic; Genetic algorithms; Neural networks
KW  -  Clinical database; Data clustering; Diabetes diagnosis; Diagnostic decisions; Hidden knowledge; Hybrid genetic algorithms; Prediction methods
KW  -  Data mining
PB  - Institute of Electrical and Electronics Engineers Inc.
A1  - Thangarasu, G.
A1  - Dominic, P.D.D.
SN  - 9781479943913
N1  - cited By 9; Conference of 2014 International Conference on Computer and Information Sciences, ICCOINS 2014 ; Conference Date: 3 June 2014 Through 5 June 2014; Conference Code:112912
AV  - none
Y1  - 2014///
TI  - Prediction of hidden knowledge from Clinical Database using data mining techniques
ER  -