ArticleClinical cardiology2024
A machine learning approach to predicting vascular calcification risk of type 2 diabetes: A retrospective study.
Article in Clinical cardiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 3 citations in OpenAlex.
- Targeting Vascular Calcification: Novel Insights into Molecular Pathways and Clinical Interventions.Cardiovascular drugs and therapy · 2026Review
- Regression analysis and validation of risk factors for upper limb dysfunction following modified radical mastectomy for breast cancer patients.American journal of translational research · 2025Article
- A machine learning approach to predicting vascular calcification risk of type 2 diabetes: A retrospective study.Clinical cardiology · 2024Article
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Authors and funding
13 authors at 5 institutions in 1 country.
Funding
Abstract
backgroundRecently, patients with type 2 diabetes mellitus (T2DM) have experienced a higher incidence and severer degree of vascular calcification (VC), which leads to an increase in the incidence and mortality of vascular complications in patients with T2DM. HYPOTHESIS: To construct and validate prediction models for the risk of VC in patients with T2DM.
methodsTwenty-three baseline demographic and clinical characteristics were extracted from the electronic medical record system. Ten clinical features were screened with least absolute shrinkage and selection operator method and were used to develop prediction models based on eight machine learning (ML) algorithms (k-nearest neighbor [k-NN], light gradient boosting machine, logistic regression [LR], multilayer perception [(MLP], Naive Bayes [NB], random forest [RF], support vector machine [SVM], XGBoost [XGB]). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, and precision.
resultsA total of 1407 and 352 patients were retrospectively collected in the training and test sets, respectively. Among the eight models, the AUC value in the NB model was higher than the other models (NB: 0.753, LGB: 0.719, LR: 0.749, MLP: 0.715, RF: 0.722, SVM: 0.689, XGB:0.707, p < .05 for all). The k-NN model achieved the highest sensitivity of 0.75 (95% confidence interval [CI]: 0.633-0.857), the MLP model achieved the highest accuracy of 0.81 (95% CI: 0.767-0.852) and specificity of 0.875 (95% CI: 0.836-0.912).
conclusionsThis study developed a predictive model of VC based on ML and clinical features in type 2 diabetic patients. The NB model is a tool with potential to facilitate clinicians in identifying VC in high-risk patients.
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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 OpenQuestion graph.