Comput Math Methods Med - Screening for prediabetes using machine learning models.

Tópicos

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Resumo

The global prevalence of diabetes is rapidly increasing. Studies support the necessity of screening and interventions for prediabetes, which could result in serious complications and diabetes. This study aimed at developing an intelligence-based screening model for prediabetes. Data from the Korean National Health and Nutrition Examination Survey (KNHANES) were used, excluding subjects with diabetes. The KNHANES 2010 data (n = 4685) were used for training and internal validation, while data from KNHANES 2011 (n = 4566) were used for external validation. We developed two models to screen for prediabetes using an artificial neural network (ANN) and support vector machine (SVM) and performed a systematic evaluation of the models using internal and external validation. We compared the performance of our models with that of a screening score model based on logistic regression analysis for prediabetes that had been developed previously. The SVM model showed the areas under the curve of 0.731 in the external datasets, which is higher than those of the ANN model (0.729) and the screening score model (0.712), respectively. The prescreening methods developed in this study performed better than the screening score model that had been developed previously and may be more effective method for prediabetes screening.

Resumo Limpo

global preval diabet rapid increas studi support necess screen intervent prediabet result serious complic diabet studi aim develop intelligencebas screen model prediabet data korean nation health nutrit examin survey knhane use exclud subject diabet knhane data n use train intern valid data knhane n use extern valid develop two model screen prediabet use artifici neural network ann support vector machin svm perform systemat evalu model use intern extern valid compar perform model screen score model base logist regress analysi prediabet develop previous svm model show area curv extern dataset higher ann model screen score model respect prescreen method develop studi perform better screen score model develop previous may effect method prediabet screen

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