Prediction of hidden knowledge from Clinical Database using data mining techniques

Author

Thangarasu, Gunasekar ; Dominic, P.D.D.

Author_Institution

Dept. of Comput. & Inf. Sci., Univ. Teknol. Petronas Bandar Seri Iskandar, Tronoh, Malaysia

fYear

2014

fDate

3-5 June 2014

Firstpage

1

Lastpage

5

Abstract

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.

Keywords

data mining; database management systems; diseases; medical computing; patient diagnosis; clinical consultation details; clinical database; data mining techniques; diabetes diagnosis methods; diabetic disease identification; diagnostic decision; disease prediction; diseases; family history; hidden knowledge prediction; medical lab report; medical test; physical examination; Databases; Diabetes; Diseases; Genetic algorithms; Medical diagnostic imaging; Neural networks; Prediction algorithms; Data Clustering Techniques; Fuzzy Logic; Hybrid Genetic Algorithm; Neural Network;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer and Information Sciences (ICCOINS), 2014 International Conference on

Conference_Location

Kuala Lumpur

Print_ISBN

978-1-4799-4391-3

Type

conf

DOI

10.1109/ICCOINS.2014.6868414

Filename

6868414