ArticleBMJ open2016
Decision tree-based modelling for identification of potential interactions between type 2 diabetes risk factors: a decade follow-up in a Middle East prospective cohort study.
Article in BMJ open, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 3 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 3 syntheses or guidelines pooled it, 49 citations in OpenAlex.
- The Use of Machine Learning for Analyzing Real-World Data in Disease Prediction and Management: Systematic Review.JMIR medical informatics · 2025Pooled it
- Accuracy of Machine Learning Classification Models for the Prediction of Type 2 Diabetes Mellitus: A Systematic Survey and Meta-Analysis Approach.International journal of environmental research and public health · 2022Pooled it
- Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making.BMC medical informatics and decision making · 2021Pooled it
- Sex-Specific Dietary Predictors of Blood Glucose Identified Through Decision Tree Modeling in Adults.Nutrients · 2025Article
- Related Risk Factors That Predict Moderate to Severe Asthma Attack in Children: Analysis Based on Logistic Regression and Decision Tree.International journal of general medicine · 2025Article
- Precision medicine in diabetes prediction: Exploring a subgroup-specific biomarker strategy for risk stratification.Journal of diabetes investigation · 2025Article
- A scoping review of artificial intelligence-based methods for diabetes risk prediction.NPJ digital medicine · 2023Article
- Using a decision tree algorithm to distinguish between repeated supra-therapeutic and acute acetaminophen exposures.BMC medical informatics and decision making · 2023Article
- Environmental exposures in machine learning and data mining approaches to diabetes etiology: A scoping review.Artificial intelligence in medicine · 2023Article
- Integrated statistical and machine learning analysis provides insight into key influencing symptoms for distinguishing early-onset type 2 diabetes.Chronic diseases and translational medicine · 2022Article
- Risk factor analysis and clinical decision tree model construction for diabetic retinopathy in Western China.World journal of diabetes · 2022Article
- COVID-19 Phenotypes and Comorbidity: A Data-Driven, Pattern Recognition Approach Using National Representative Data from the United States.International journal of environmental research and public health · 2022Article
- Sense and Learn: Recent Advances in Wearable Sensing and Machine Learning for Blood Glucose Monitoring and Trend-Detection.Frontiers in bioengineering and biotechnology · 2022Review
- Data mining in clinical big data: the frequently used databases, steps, and methodological models.Military Medical Research · 2021Review
- Analysis of risk factors for carotid intima-media thickness in patients with type 2 diabetes mellitus in Western China assessed by logistic regression combined with a decision tree model.Diabetology & metabolic syndrome · 2020Article
- Machine Learning in Relation to Emergency Medicine Clinical and Operational Scenarios: An Overview.The western journal of emergency medicine · 2019Review
- Diabetes Mellitus: Findings from 20 Years of the Tehran Lipid and Glucose Study.International journal of endocrinology and metabolism · 2018Review
- Application of survival tree analysis for exploration of potential interactions between predictors of incident chronic kidney disease: a 15-year follow-up study.Journal of translational medicine · 2017Article
- A new look at risk patterns related to coronary heart disease incidence using survival tree analysis: 12 Years Longitudinal Study.Scientific reports · 2017Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe current study was undertaken for use of the decision tree (DT) method for development of different prediction models for incidence of type 2 diabetes (T2D) and for exploring interactions between predictor variables in those models.
designProspective cohort study.
settingTehran Lipid and Glucose Study (TLGS).
methodsA total of 6647 participants (43.4% men) aged >20 years, without T2D at baselines ((1999-2001) and (2002-2005)), were followed until 2012. 2 series of models (with and without 2-hour postchallenge plasma glucose (2h-PCPG)) were developed using 3 types of DT algorithms. The performances of the models were assessed using sensitivity, specificity, area under the ROC curve (AUC), geometric mean (G-Mean) and F-Measure. PRIMARY OUTCOME MEASURE: T2D was primary outcome which defined if fasting plasma glucose (FPG) was ≥7 mmol/L or if the 2h-PCPG was ≥11.1 mmol/L or if the participant was taking antidiabetic medication.
resultsDuring a median follow-up of 9.5 years, 729 new cases of T2D were identified. The Quick Unbiased Efficient Statistical Tree (QUEST) algorithm had the highest sensitivity and G-Mean among all the models for men and women. The models that included 2h-PCPG had sensitivity and G-Mean of (78% and 0.75%) and (78% and 0.78%) for men and women, respectively. Both models achieved good discrimination power with AUC above 0.78. FPG, 2h-PCPG, waist-to-height ratio (WHtR) and mean arterial blood pressure (MAP) were the most important factors to incidence of T2D in both genders. Among men, those with an FPG≤4.9 mmol/L and 2h-PCPG≤7.7 mmol/L had the lowest risk, and those with an FPG>5.3 mmol/L and 2h-PCPG>4.4 mmol/L had the highest risk for T2D incidence. In women, those with an FPG≤5.2 mmol/L and WHtR≤0.55 had the lowest risk, and those with an FPG>5.2 mmol/L and WHtR>0.56 had the highest risk for T2D incidence.
conclusionsOur study emphasises the utility of DT for exploring interactions between predictor variables.
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