Decision trees as a simple-to-use and reliable tool to identify individuals with impaired glucose metabolism or type 2 diabetes mellitus

Abstract

Objective: The prevalence of unknown impaired fasting glucose (IFG), impaired glucose tolerance (IGT), or type 2 diabetes mellitus (T2DM) is high. Numerous studies demonstrated that IFG, IGT, or T2DM are associated with increased cardiovascular risk, therefore an improved identification strategy would be desirable. The objective of this study was to create a simple and reliable tool to identify individuals with impaired glucose metabolism (IGM). Design and methods: A cohort of 1737 individuals (1055 controls, 682 with previously unknown IGM) was screened by 75 g oral glucose tolerance test (OGTT). Supervised machine learning was used to automatically generate decision trees to identify individuals with IGM. To evaluate the accuracy of identification, a tenfold cross-validation was performed. Resulting trees were subsequently re-evaluated in a second, independent cohort of 1998 individuals (1253 controls, 745 unknown IGM). Results: A clinical decision tree included age and systolic blood pressure (sensitivity 89.3%, specificity 37.4%, and positive predictive value (PPV) 48.0%), while a tree based on clinical and laboratory data included fasting glucose and systolic blood pressure (sensitivity 89.7%, specificity 54.6%, and PPV 56.2%). The inclusion of additional parameters did not improve test quality. The external validation approach confirmed the presented decision trees. Conclusion: We proposed a simple tool to identify individuals with existing IGM. From a practical perspective, fasting blood glucose and blood pressure measurements should be regularly measured in all individuals presenting in outpatient clinics. An OGTT appears to be useful only if the subjects are older than 48 years or show abnormalities in fasting glucose or blood pressure.

Defining the rehabilitation treatment programs for stroke patients by applying Neural Network and Decision Trees models

“…DT model is a common method that is applied in many studies [17,19]. Most studies have utilized this algorithm for diagnosis the disease.…”

“…Most studies have utilized this algorithm for diagnosis the disease. For example, M. Hische and his colleagues [19] used DT model to identify patients with type 2 diabetes mellitus. However, there were a few studies that have applied this model to identify the treatment program to patients.…”

Skin Autofluorescence Based Decision Tree in Detection of Impaired Glucose Tolerance and Diabetes

“…A limitation of the decision model is that information on use of antihypertensive medication, and on family history of diabetes may be unreliable. Recent decision trees with good performance for identifying individuals with IGT/diabetes have used systolic blood pressure besides age [20].…”

“…Accounting such alternative reasons for elevated skin AF might improve specificity for diabetes and IGT detection especially in intermediate risk persons with comorbidity. On the other hand, decision trees (including age and systolic bloodpressure) have also been proposed as simple and reliable tools for identifying individuals with IGT or T2DM [20], while other well-known risk factors for IGT/DM such as BMI, and family history of diabetes are represented in most of the diabetes screening questionnaires [8], [9]. We assumed that the performance of noninvasive skin AF-based DM/IGT decision tree might also be improved by integrating such easily available items.…”

A distinct metabolic signature predicts development of fasting plasma glucose

“…Nevertheless, this is a topic that is subject to continuing discussion [2-5]. Established classical markers include: family history of diabetes, markers of adiposity, age and glycemic control itself.…”