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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Kowsar
2015
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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 |
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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 |
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