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- W4289260193 abstract "Abstract The acute rise in childhood obesity has been associated with an increased risk of metabolic dysfunction and early-onset diabetes. Metabolic syndrome (MetS), a cluster of risk factors predicting future cardiometabolic disease, is not well established in children. This study leverages data from a longitudinal observational study of diabetes (1965–2007) that enrolled pediatric and adult participants, to create machine learning-based models (ML) to predict future incident diabetes. Anthropometric, clinical, and laboratory data from baseline examinations of 2,049 nondiabetic children were obtained, and 17-metabolic risk predictor variables were included. Multiple classification schemes were explored, and an ensemble model was created by combining the 5-best algorithms with the highest predictive accuracy. The area under the receiver-operating-characteristic curve (AUROC) of the ensemble model was significantly higher than the logistic regression model in predicting future diabetes [0.88 (95% CI: 0.85–0.9) and 0.63 (95% CI: 0.57–0.69), (p < 0.001) ]. The predictor variable importance of the best performing Random Forest model identified additional risk predictors outside of the pediatric MetS components as recommended by the International Diabetes Federation. This study used consistent and well-structured clinical data to create ML-based models that predicted future incident diabetes in a pediatric cohort with a high degree of accuracy." @default.
- W4289260193 created "2022-08-01" @default.
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- W4289260193 date "2022-08-01" @default.
- W4289260193 modified "2023-09-27" @default.
- W4289260193 title "Predicting Future Diabetes Using Machine Learning Models in a High-Risk Pediatric Cohort" @default.
- W4289260193 doi "https://doi.org/10.21203/rs.3.rs-1891969/v1" @default.
- W4289260193 hasPublicationYear "2022" @default.
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