ArticleInternational journal of endocrinology and metabolism2015
An application of association rule mining to extract risk pattern for type 2 diabetes using tehran lipid and glucose study database.
Article in International journal of endocrinology and metabolism, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 40 citations in OpenAlex.
- Association rule mining of time-based patterns in diabetes-related comorbidities on imbalanced data: a pre- and post-diagnosis study.BMC medical informatics and decision making · 2025Article
- The diabetes mellitus multimorbidity network in hospitalized patients over 50 years of age in China: data mining of medical records.BMC public health · 2024Article
- Environmental exposures in machine learning and data mining approaches to diabetes etiology: A scoping review.Artificial intelligence in medicine · 2023Article
- Associations Among Multimorbid Conditions in Hospitalized Middle-aged and Older Adults in China: Statistical Analysis of Medical Records.JMIR public health and surveillance · 2022Article
- Markers of insulin resistance in Polycystic ovary syndrome women: An update.World journal of diabetes · 2022Review
- Mining associations between glycemic variability in awake-time and in-sleep among non-diabetic adults.Frontiers in medical technology · 2022Article
- A survey on data mining techniques used in medicine.Journal of diabetes and metabolic disorders · 2021Article
- Article
- Contributions and Implications of the Tehran Lipid and Glucose Study.International journal of endocrinology and metabolism · 2018Review
- Evaluation of rational nonsteroidal anti-inflammatory drugs and gastro-protective agents use; association rule data mining using outpatient prescription patterns.BMC medical informatics and decision making · 2017Article
- Machine Learning and Data Mining Methods in Diabetes Research.Computational and structural biotechnology journal · 2017Review
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundType 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.
objectivesThe aim of this study was to identify risk patterns for type 2 diabetes incidence using association rule mining (ARM). PATIENTS AND
methodsA 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.
resultsIn 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.
conclusionsOur 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.
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