Authors:
Dan Bi Kim
;
Ju-young Lee
;
Eun-jung Jang
;
Ji-young Lee
and
Taek Kim
Affiliation:
Division of Bio-Medical Informatics, Center for Genome Science, Korea Centers for Disease Control and Prevention, Korea National Institute of Health, Korea, Republic of
Keyword(s):
Type 2 Diabetes Mellitus, Early Prediction System, KoGES.
Related
Ontology
Subjects/Areas/Topics:
Biomedical Engineering
;
Decision Support Systems
;
Health Information Systems
;
Online Medical Applications
Abstract:
This paper describes the implementation of an early prediction system for Type 2 diabetes mellitus. Type 2 diabetes mellitus is a multifactorial disease. It is not only associated with an unhealthy lifestyle but also has a strong genetic component. Accordingly, in order to decrease an incidence rate of T2DM, it is important to predict T2DM risk with using multifactors which are supposed to affect T2DM. We have implemented a prediction system for T2DM, and it employs several statistical prediction models. These models are produced by statistical analysis about cohort data of Korean Genome and Epidemiology Study (KoGES), and include risk factors which are adequate for preventing T2DM in Korean populations. The prediction system is written in JSF and Java, and developed into web application which is designed through object oriented modeling. Web application of this system offers user interfaces in order to input data which is needed for predicting risk group, select predefined predictio
n models, and so on. The system provides the results which are predicted by selected models using inputted information.
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