ArticleTechnology and health care : official journal of the European Society for Engineering and Medicine2024
A machine learning prediction model for cancer risk in patients with type 2 diabetes based on clinical tests.
Article in Technology and health care : official journal of the European Society for Engineering and Medicine, 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.
- Characterizing Acute Pulmonary Embolism After Off-Pump Coronary Artery Bypass Surgery Using a Predictive XGBoost Model.Journal of multidisciplinary healthcare · 2026Article
- Risk factors for cancer among patients with type 2 diabetes: a retrospective cohort study.BMC cancer · 2025Article
- Development of a 5-Year Risk Prediction Model for Transition From Prediabetes to Diabetes Using Machine Learning: Retrospective Cohort Study.Journal of medical Internet research · 2025Article
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Authors and funding
9 authors.
Funding
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Abstract
backgroundThe incidence of type 2 diabetes is rapidly increasing worldwide. Studies have shown that it is also associated with cancer-related morbidities. Early detection of cancer in patients with type 2 diabetes is crucial.
objectiveThis study aimed to construct a model to predict cancer risk in patients with type 2 diabetes.
methodsThis study collected clinical data from a total of 5198 patients. A cancer risk prediction model was established by analyzing 261 items from routine laboratory tests. We screened 107 risk factors from 261 clinical tests based on the importance of the characteristic variables, significance of differences between groups (P< 0.05), and minimum description length algorithm.
resultsCompared with 16 machine learning classifiers, five classifiers based on the decision tree algorithm (CatBoost, light gradient boosting, random forest, XGBoost, and gradient boosting) had an area under the receiver operating characteristic curve (AUC) of > 0.80. The AUC for CatBoost was 0.852 (sensitivity: 79.6%; specificity: 83.2%).
conclusionThe constructed model can predict the risk of cancer in patients with type 2 diabetes based on tumor biomarkers and routine tests using machine learning algorithms. This is helpful for early cancer risk screening and prevention to improve patient outcomes.
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