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Evaluating Machine Learning Models for Classifying Diabetes Using Demographic, Clinical, Lifestyle, Anthropometric,

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This study introduces a machine learning model that improves diabetes classification by including environmental exposure biomarkers alongside traditional health data. The enhanced model shows better accuracy in identifying diabetes risk in adults.

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ROC–AUCSMOTEenvironmental exposureexposomemachine learningmultiple imputation (MICE)predictive modelingrandom forest

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Area of Science:

  • Environmental Health
  • Data Science
  • Biomedical Informatics

Background:

  • Diabetes mellitus is a complex disease influenced by clinical, metabolic, lifestyle, demographic, and environmental factors.
  • Current diabetes classification models often overlook environmental exposure biomarkers, relying primarily on biomedical indicators.
  • Integrating diverse data sources is crucial for a comprehensive understanding of diabetes development.

Purpose of the Study:

  • To develop and evaluate a supervised machine learning classification framework for physician-diagnosed diabetes.
  • To assess the impact of integrating environmental exposure biomarkers with demographic, anthropometric, clinical, and behavioral features.
  • To compare the performance of eight different supervised classifiers using National Health and Nutrition Examination Survey (NHANES) data.

Main Methods:

  • Utilized NHANES 2017-2018 data for adults aged 18 years and older.
  • Addressed missing data using Multiple Imputation by Chained Equations and corrected class imbalance with Synthetic Minority Oversampling Technique.
  • Evaluated eight supervised classifiers via stratified ten-fold cross-validation and an independent 80/20 hold-out set.

Main Results:

  • Random Forest and XGBoost demonstrated superior performance with ROC AUC values of 0.891 and 0.885, respectively, after data preprocessing.
  • Feature importance analysis highlighted age, household income, and waist circumference as key predictors.
  • XGBoost achieved the highest overall accuracy and F1-score, while Random Forest showed the greatest sensitivity in out-of-sample evaluation.

Conclusions:

  • Incorporating environmental exposure biomarkers significantly enhances the classification performance for physician-diagnosed diabetes.
  • The findings advocate for the inclusion of chemical exposure variables in population-level diabetes risk stratification.
  • Integrating heterogeneous feature sets, including environmental data, is valuable for machine learning-based health risk assessment.