Cargando…

An Application of Association Rule Mining to Extract Risk Pattern for Type 2 Diabetes Using Tehran Lipid and Glucose Study Database

BACKGROUND: Type 2 diabetes, common and serious global health concern, had an estimated worldwide prevalence of 366 million in 2011, which is expected to rise to 552 million people, by 2030, unless urgent action is taken. OBJECTIVES: The aim of this study was to identify risk patterns for type 2 dia...

Descripción completa

Detalles Bibliográficos
Autores principales: Ramezankhani, Azra, Pournik, Omid, Shahrabi, Jamal, Azizi, Fereidoun, Hadaegh, Farzad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Kowsar 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4393501/
https://www.ncbi.nlm.nih.gov/pubmed/25926855
http://dx.doi.org/10.5812/ijem.25389
_version_ 1782366167538597888
author Ramezankhani, Azra
Pournik, Omid
Shahrabi, Jamal
Azizi, Fereidoun
Hadaegh, Farzad
author_facet Ramezankhani, Azra
Pournik, Omid
Shahrabi, Jamal
Azizi, Fereidoun
Hadaegh, Farzad
author_sort Ramezankhani, Azra
collection PubMed
description BACKGROUND: Type 2 diabetes, common and serious global health concern, had an estimated worldwide prevalence of 366 million in 2011, which is expected to rise to 552 million people, by 2030, unless urgent action is taken. OBJECTIVES: The aim of this study was to identify risk patterns for type 2 diabetes incidence using association rule mining (ARM). PATIENTS AND METHODS: A population of 6647 individuals without diabetes, aged ≥ 20 years at inclusion, was followed for 10-12 years, to analyze risk patterns for diabetes occurrence. Study variables included demographic and anthropometric characteristics, smoking status, medical and drug history and laboratory measures. RESULTS: In the case of women, the results showed that impaired fasting glucose (IFG) and impaired glucose tolerance (IGT), in combination with body mass index (BMI) ≥ 30 kg/m(2), family history of diabetes, wrist circumference > 16.5 cm and waist to height ≥ 0.5 can increase the risk for developing diabetes. For men, a combination of IGT, IFG, length of stay in the city (> 40 years), central obesity, total cholesterol to high density lipoprotein ratio ≥ 5.3, low physical activity, chronic kidney disease and wrist circumference > 18.5 cm were identified as risk patterns for diabetes occurrence. CONCLUSIONS: Our study showed that ARM is a useful approach in determining which combinations of variables or predictors occur together frequently, in people who will develop diabetes. The ARM focuses on joint exposure to different combinations of risk factors, and not the predictors alone.
format Online
Article
Text
id pubmed-4393501
institution National Center for Biotechnology Information
language English
publishDate 2015
publisher Kowsar
record_format MEDLINE/PubMed
spelling pubmed-43935012015-04-29 An Application of Association Rule Mining to Extract Risk Pattern for Type 2 Diabetes Using Tehran Lipid and Glucose Study Database Ramezankhani, Azra Pournik, Omid Shahrabi, Jamal Azizi, Fereidoun Hadaegh, Farzad Int J Endocrinol Metab Research Article BACKGROUND: Type 2 diabetes, common and serious global health concern, had an estimated worldwide prevalence of 366 million in 2011, which is expected to rise to 552 million people, by 2030, unless urgent action is taken. OBJECTIVES: The aim of this study was to identify risk patterns for type 2 diabetes incidence using association rule mining (ARM). PATIENTS AND METHODS: A population of 6647 individuals without diabetes, aged ≥ 20 years at inclusion, was followed for 10-12 years, to analyze risk patterns for diabetes occurrence. Study variables included demographic and anthropometric characteristics, smoking status, medical and drug history and laboratory measures. RESULTS: In the case of women, the results showed that impaired fasting glucose (IFG) and impaired glucose tolerance (IGT), in combination with body mass index (BMI) ≥ 30 kg/m(2), family history of diabetes, wrist circumference > 16.5 cm and waist to height ≥ 0.5 can increase the risk for developing diabetes. For men, a combination of IGT, IFG, length of stay in the city (> 40 years), central obesity, total cholesterol to high density lipoprotein ratio ≥ 5.3, low physical activity, chronic kidney disease and wrist circumference > 18.5 cm were identified as risk patterns for diabetes occurrence. CONCLUSIONS: Our study showed that ARM is a useful approach in determining which combinations of variables or predictors occur together frequently, in people who will develop diabetes. The ARM focuses on joint exposure to different combinations of risk factors, and not the predictors alone. Kowsar 2015-04-30 /pmc/articles/PMC4393501/ /pubmed/25926855 http://dx.doi.org/10.5812/ijem.25389 Text en Copyright © 2015, Research Institute For Endocrine Sciences and Iran Endocrine Society. http://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.
spellingShingle Research Article
Ramezankhani, Azra
Pournik, Omid
Shahrabi, Jamal
Azizi, Fereidoun
Hadaegh, Farzad
An Application of Association Rule Mining to Extract Risk Pattern for Type 2 Diabetes Using Tehran Lipid and Glucose Study Database
title An Application of Association Rule Mining to Extract Risk Pattern for Type 2 Diabetes Using Tehran Lipid and Glucose Study Database
title_full An Application of Association Rule Mining to Extract Risk Pattern for Type 2 Diabetes Using Tehran Lipid and Glucose Study Database
title_fullStr An Application of Association Rule Mining to Extract Risk Pattern for Type 2 Diabetes Using Tehran Lipid and Glucose Study Database
title_full_unstemmed An Application of Association Rule Mining to Extract Risk Pattern for Type 2 Diabetes Using Tehran Lipid and Glucose Study Database
title_short An Application of Association Rule Mining to Extract Risk Pattern for Type 2 Diabetes Using Tehran Lipid and Glucose Study Database
title_sort application of association rule mining to extract risk pattern for type 2 diabetes using tehran lipid and glucose study database
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4393501/
https://www.ncbi.nlm.nih.gov/pubmed/25926855
http://dx.doi.org/10.5812/ijem.25389
work_keys_str_mv AT ramezankhaniazra anapplicationofassociationruleminingtoextractriskpatternfortype2diabetesusingtehranlipidandglucosestudydatabase
AT pournikomid anapplicationofassociationruleminingtoextractriskpatternfortype2diabetesusingtehranlipidandglucosestudydatabase
AT shahrabijamal anapplicationofassociationruleminingtoextractriskpatternfortype2diabetesusingtehranlipidandglucosestudydatabase
AT azizifereidoun anapplicationofassociationruleminingtoextractriskpatternfortype2diabetesusingtehranlipidandglucosestudydatabase
AT hadaeghfarzad anapplicationofassociationruleminingtoextractriskpatternfortype2diabetesusingtehranlipidandglucosestudydatabase
AT ramezankhaniazra applicationofassociationruleminingtoextractriskpatternfortype2diabetesusingtehranlipidandglucosestudydatabase
AT pournikomid applicationofassociationruleminingtoextractriskpatternfortype2diabetesusingtehranlipidandglucosestudydatabase
AT shahrabijamal applicationofassociationruleminingtoextractriskpatternfortype2diabetesusingtehranlipidandglucosestudydatabase
AT azizifereidoun applicationofassociationruleminingtoextractriskpatternfortype2diabetesusingtehranlipidandglucosestudydatabase
AT hadaeghfarzad applicationofassociationruleminingtoextractriskpatternfortype2diabetesusingtehranlipidandglucosestudydatabase