scholarly journals Semi supervised data mining model for the prognosis of pre-diabetic conditions in type 2 Diabetes Mellitus

2019 ◽  
Vol 15 (12) ◽  
pp. 875-881 ◽  
Author(s):  
A Sumathi ◽  
Keyword(s):  
Diabetes Mellitus ◽  
Data Mining ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Mining Model

Use of Data Mining in the prediction of risk factors of Type 2 diabetes mellitus in Gulf countries

2021 ◽  
Vol 8 (4) ◽  
pp. 638-645
Author(s):  
W. Boutayeb ◽  
◽  
M. Badaoui ◽  
H. Al Ali ◽  
A. Boutayeb ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Data Mining ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Gulf Coast ◽  
Use Of Data ◽  
Gulf Countries ◽  
Unhealthy Diet

Prevalence of diabetes in Gulf countries is knowing a significant increase because of various risk factors, such as: obesity, unhealthy diet, physical inactivity and smoking. The aim of our proposed study is to use Data Mining and Data Analysis tools in order to determine different risk factors of the development of Type~2 diabetes mellitus (T2DM) in Gulf countries, from Gulf COAST dataset.

scholarly journals Type 2 diabetes mellitus prediction model based on data mining

2018 ◽  
Vol 10 ◽  
pp. 100-107 ◽  
Author(s):  
Han Wu ◽  
Shengqi Yang ◽  
Zhangqin Huang ◽  
Jian He ◽  
Xiaoyi Wang
Keyword(s):  
Diabetes Mellitus ◽  
Data Mining ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Prediction Model ◽  
Model Based

scholarly journals Risk Assessment of Sarcopenia in Patients With Type 2 Diabetes Mellitus Using Data Mining Methods

2020 ◽  
Vol 11 ◽  
Author(s):  
Mengzhao Cui ◽  
Xiaokun Gang ◽  
Fang Gao ◽  
Gang Wang ◽  
Xianchao Xiao ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Data Mining ◽  
Type 2 Diabetes ◽  
Risk Assessment ◽  
Type 2 Diabetes Mellitus ◽  
Mining Methods ◽  
Using Data

scholarly journals Design of a geospatial model applied to Health management

2019 ◽  
Vol 72 (2) ◽  
pp. 420-426
Author(s):  
Marcelo Rosano Dallagassa ◽  
Franciele Iachecen ◽  
Deborah Ribeiro Carvalho ◽  
Sergio Ossamu Ioshii
Keyword(s):  
Diabetes Mellitus ◽  
Data Mining ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Health Plan ◽  
Health Management ◽  
Initial Step ◽  
Geographical Information ◽  
Data Mining Technique

ABSTRACT Objective: To identify geographically the beneficiaries categorized as prone to Type 2 Diabetes Mellitus, using the recognition of patterns in a database of a health plan operator, through data mining. Method: The following steps were developed: the initial step, the information survey. Development, construction of the process of extraction, transformation, and loading of the database. Deployment, presentation of the geographical information through a georeferencing tool. Results: As a result, the mapping of Paraná according to its health care network and the concentration of Type 2 Diabetes Mellitus is presented, enabling the identification of cause-and-effect relationships. Conclusion: It is concluded that the analysis of georeferenced information, linked to health information obtained through the data mining technique, can be an excellent tool for the health management of a health plan operator, contributing to the decision-making process in Health.

scholarly journals Comorbidity Study on Type 2 Diabetes Mellitus Using Data Mining

2012 ◽  
Vol 27 (2) ◽  
pp. 197 ◽  
Author(s):  
Hye Soon Kim ◽  
A Mi Shin ◽  
Mi Kyung Kim ◽  
Yoon Nyun Kim
Keyword(s):  
Diabetes Mellitus ◽  
Data Mining ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Using Data
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