ArticleInternational journal of environmental research and public health2021
Predicting Type 2 Diabetes Using Logistic Regression and Machine Learning Approaches.
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 1 of them a synthesis that pooled it.
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Who cites it
55 citing papers in PubMed, 1 synthesis or guideline pooled it, 238 citations in OpenAlex.
- Machine learning and artificial intelligence in type 2 diabetes prediction: a comprehensive 33-year bibliometric and literature analysis.Frontiers in digital health · 2025Pooled it
- Exploring the correlates of diabetes-related stigma in young and middle-aged adults with type 2 diabetes: A cross-sectional study.Medicine · 2026Article
- Plasminogen activator inhibitor-1, vaspin, and dietary inflammatory index in relation to cardiometabolic health in diabetic women.Frontiers in nutrition · 2026Article
- Machine learning predicts diabetes risk in high-risk populations: analysis of National Health and Nutrition Examination Survey data.Archives of medical science : AMS · 2026Article
- Machine learning models for predicting new-onset diabetes following acute pancreatitis using real-world data.BMJ public health · 2026Article
- Machine-learning-based prediction model of type 2 diabetes using liver enzymes: a cross-sectional study.Frontiers in endocrinology · 2026Article
- Development and Internal Validation of an Explainable Machine Learning Model for Physical Activity Adherence Among Community-Dwelling Patients with Type 2 Diabetes Mellitus.Patient preference and adherence · 2026Article
- Development of a machine learning-based interface for insulin dependency prediction using clinical data.Scientific reports · 2025Article
- Prediction Model of Intradialytic Hypertension in Hemodialysis Patients Based on Machine Learning.Journal of medical systems · 2025Article
- AI-driven prediction of insulin resistance in non-diabetic populations using minimal invasive tests: comparing models and criteria.Diabetology & metabolic syndrome · 2025Article
- Transfer learning prediction of type 2 diabetes with unpaired clinical and genetic data.Scientific reports · 2025Article
- Heterogeneous Association Between E-Cigarette Use and Diabetes Prevalence Among U.S. Adults.AJPM focus · 2025Article
- Scalable and robust machine learning framework for HIV classification using clinical and laboratory data.Scientific reports · 2025Article
- Utility of long-term systolic blood pressure variability for predicting the development of type 2 diabetes mellitus.Nagoya journal of medical science · 2025Article
- Statistical models versus machine learning approach for competing risks in proctological surgery.Updates in surgery · 2025Article
- Using a robust model to detect the association between anthropometric factors and T2DM: machine learning approaches.BMC medical informatics and decision making · 2025Article
- DiabetesXpertNet: An innovative attention-based CNN for accurate type 2 diabetes prediction.PloS one · 2025Article
- Development and validation of predictive models for diabetic retinopathy using machine learning.PloS one · 2025Article
- Energy landscape analysis of health checkup data clarified multiple pathways to diabetes development in obese and non-obese subjects.Frontiers in endocrinology · 2025Observational
- Optimising test intervals for individuals with type 2 diabetes: A machine learning approach.PloS one · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetes mellitus is one of the most common human diseases worldwide and may cause several health-related complications. It is responsible for considerable morbidity, mortality, and economic loss. A timely diagnosis and prediction of this disease could provide patients with an opportunity to take the appropriate preventive and treatment strategies. To improve the understanding of risk factors, we predict type 2 diabetes for Pima Indian women utilizing a logistic regression model and decision tree-a machine learning algorithm. Our analysis finds five main predictors of type 2 diabetes: glucose, pregnancy, body mass index (BMI), diabetes pedigree function, and age. We further explore a classification tree to complement and validate our analysis. The six-fold classification tree indicates glucose, BMI, and age are important factors, while the ten-node tree implies glucose, BMI, pregnancy, diabetes pedigree function, and age as the significant predictors. Our preferred specification yields a prediction accuracy of 78.26% and a cross-validation error rate of 21.74%. We argue that our model can be applied to make a reasonable prediction of type 2 diabetes, and could potentially be used to complement existing preventive measures to curb the incidence of diabetes and reduce associated costs.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.