A combined strategy of feature selection and machine learning to identify predictors of prediabetes

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.

Nutritional markers of undiagnosed type 2 diabetes in adults: Findings of a machine learning analysis with external validation and benchmarking

“…This study demonstrated that the proof-of-concept ML workflow previously proposed by us [32] is viable, when applied to different research contexts with appropriate modifications, indicating a high degree of generalisability and adaptability. As the obstacles to implementing ML interventions in healthcare are widespread and systemic [62], those requiring only customarily compiled health information would offer more realistic solutions.…”

“…The analytic workflow of this study was based on our previously published proof-of-study exploring predictors of prediabetes [ 32 ]. However, substantial modifications were made including analysing nutritional variables (omitted in the previous study) and excluding serum biomarkers in order to consider only those predictors which are simple, scalable and based on self-reported or easily measurable parameters.…”

Type 2 Diabetes with Artificial Intelligence Machine Learning: Methods and Evaluation

“…Diagnosis of type 2 diabetes if not performed at an early stage can lead to several serious life-threatening complications [6]. Machine learning-based decision support systems for the prediction of chronic diseases [7][8][9][10][11][12] have thus gained a lot of attention for better prognosis/diagnosis support to health professionals and public health [13]. Several research efforts have been proposed in the literature for using machine learning classification algorithms to predict the prevalence of type 2 diabetes based on different risk factors .…”

Machine Learning Applications in Endocrinology and Metabolism Research: An Overview

“…It should be noted that the selected studies might not be sufficient to reflect an entire trend of ML applications in the endocrinology field, but it can provide practical examples for understanding the utility of ML algorithms applied to various fields of endocrine researches. The details of the reviewed studies are summarized in Table 2 [4][5][6][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. www.…”