Kushan De Silva1,2, Daniel Jönsson3, Ryan T Demmer4. 1. Department of Clinical Sciences, Faculty of Medicine, Lund University, Lund,Sweden. 2. Department of General Practice, School of Primary and Allied Health Care, Faculty of Medicine, Nursing, and Health Sciences, Monash University, Notting Hill, Australia. 3. Department of Periodontology, Malmö University, Malmö and Swedish Dental Service of Skane, Lund, Sweden. 4. Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA.
Abstract
OBJECTIVE: To identify predictors of prediabetes using feature selection and machine learning on a nationally representative sample of the US population. MATERIALS AND METHODS: We analyzed n = 6346 men and women enrolled in the National Health and Nutrition Examination Survey 2013-2014. Prediabetes was defined using American Diabetes Association guidelines. The sample was randomly partitioned to training (n = 3174) and internal validation (n = 3172) sets. Feature selection algorithms were run on training data containing 156 preselected exposure variables. Four machine learning algorithms were applied on 46 exposure variables in original and resampled training datasets built using 4 resampling methods. Predictive models were tested on internal validation data (n = 3172) and external validation data (n = 3000) prepared from National Health and Nutrition Examination Survey 2011-2012. Model performance was evaluated using area under the receiver operating characteristic curve (AUROC). Predictors were assessed by odds ratios in logistic models and variable importance in others. The Centers for Disease Control (CDC) prediabetes screening tool was the benchmark to compare model performance. RESULTS: Prediabetes prevalence was 23.43%. The CDC prediabetes screening tool produced 64.40% AUROC. Seven optimal (≥ 70% AUROC) models identified 25 predictors including 4 potentially novel associations; 20 by both logistic and other nonlinear/ensemble models and 5 solely by the latter. All optimal models outperformed the CDC prediabetes screening tool (P < 0.05). DISCUSSION: Combined use of feature selection and machine learning increased predictive performance outperforming the recommended screening tool. A range of predictors of prediabetes was identified. CONCLUSION: This work demonstrated the value of combining feature selection with machine learning to identify a wide range of predictors that could enhance prediabetes prediction and clinical decision-making.
OBJECTIVE: To identify predictors of prediabetes using feature selection and machine learning on a nationally representative sample of the US population. MATERIALS AND METHODS: We analyzed n = 6346 men and women enrolled in the National Health and Nutrition Examination Survey 2013-2014. Prediabetes was defined using American Diabetes Association guidelines. The sample was randomly partitioned to training (n = 3174) and internal validation (n = 3172) sets. Feature selection algorithms were run on training data containing 156 preselected exposure variables. Four machine learning algorithms were applied on 46 exposure variables in original and resampled training datasets built using 4 resampling methods. Predictive models were tested on internal validation data (n = 3172) and external validation data (n = 3000) prepared from National Health and Nutrition Examination Survey 2011-2012. Model performance was evaluated using area under the receiver operating characteristic curve (AUROC). Predictors were assessed by odds ratios in logistic models and variable importance in others. The Centers for Disease Control (CDC) prediabetes screening tool was the benchmark to compare model performance. RESULTS:Prediabetes prevalence was 23.43%. The CDC prediabetes screening tool produced 64.40% AUROC. Seven optimal (≥ 70% AUROC) models identified 25 predictors including 4 potentially novel associations; 20 by both logistic and other nonlinear/ensemble models and 5 solely by the latter. All optimal models outperformed the CDC prediabetes screening tool (P < 0.05). DISCUSSION: Combined use of feature selection and machine learning increased predictive performance outperforming the recommended screening tool. A range of predictors of prediabetes was identified. CONCLUSION: This work demonstrated the value of combining feature selection with machine learning to identify a wide range of predictors that could enhance prediabetes prediction and clinical decision-making.
Authors: Joon Young Kim; Michael I Goran; Claudia M Toledo-Corral; Marc J Weigensberg; Gabriel Q Shaibi Journal: Pediatr Diabetes Date: 2014-11-11 Impact factor: 4.866
Authors: Jeffrey P Anderson; Jignesh R Parikh; Daniel K Shenfeld; Vladimir Ivanov; Casey Marks; Bruce W Church; Jason M Laramie; Jack Mardekian; Beth Anne Piper; Richard J Willke; Dale A Rublee Journal: J Diabetes Sci Technol Date: 2015-12-20
Authors: Kushan De Silva; Siew Lim; Aya Mousa; Helena Teede; Andrew Forbes; Ryan T Demmer; Daniel Jönsson; Joanne Enticott Journal: PLoS One Date: 2021-05-05 Impact factor: 3.240