ArticleFrontiers in endocrinology2026
Machine learning screening of risk factors for diabetic microvascular complications and construction of a gradient boosting decision tree predictive model.
Article in Frontiers in endocrinology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To develop a machine learning-based classification model to aid in the early diagnosis of diabetic microvascular complications. Methods: This study analyzed clinical and laboratory data from 1,498 patients, categorized into two groups: diabetes alone and diabetes with microvascular complications. Independent risk factors for complications were identified through intergroup comparison, collinearity analysis, and logistic regression. Nine machine learning models were subsequently developed and compared. A comprehensive evaluation of the binary classification performance of the Gradient Boosting Decision Tree (GBDT) model was performed. Results: Urea, fibrinogen (FIB), prothrombin time (PT), D-dimer (DD), creatine kinase MB isoenzyme (CKMB), lipoprotein(a) (Lpa), activated partial thromboplastin time (APTT), triglycerides (TG), and cholinesterase (CHE) were identified as independent risk factors for diabetic microvascular complications. Among the nine predictive models constructed, the GBDT model demonstrated superior performance across multiple metrics, including the area under the receiver operating characteristic curve (AUC) and sensitivity, indicating strong generalization ability on the validation set. Further evaluation confirmed its consistent and robust predictive performance across training, validation, and test datasets. Calibration curve analysis showed good agreement between predicted probabilities and actual outcomes. Decision curve analysis demonstrated the model's clinical utility, and the Kolmogorov-Smirnov (KS) curve indicated excellent discriminatory power. Conclusion: The GBDT model, constructed based on the identified risk factors, exhibits outstanding predictive performance and promising application potential. It provides important theoretical support and a practical tool for the early identification and targeted intervention of diabetic microvascular complications.
Indexed as
Identifiers
What OpenQuestion holds
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 OpenQuestion graph.