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Occupational and Physical Activity Factors as Predictors of Prediabetes: A Machine Learning Study With SHAP-Based

Jun-Hee Kim Jh Kim1

  • 1From the 1, Yeonsedae-gil, Heungeop-myeon, Wonju-si, Gangwon-do 26493 South Korea (J.H.K.); and Department of Physical Therapy, College of Software and Digital Healthcare Convergence, Yonsei University, Wonju, South Korea (J.H.K.).

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Occupational and physical activity significantly predict prediabetes risk. Key factors include occupation type, working hours, and walking frequency, informing early lifestyle interventions for diabetes prevention.

Keywords:
machine learningoccupational healthphysical activityprediabetesprediction

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Area of Science:

  • Public Health
  • Epidemiology
  • Biostatistics

Background:

  • Prediabetes poses a significant public health challenge, increasing the risk of type 2 diabetes.
  • Nonclinical factors like occupation and physical activity are increasingly recognized as crucial in diabetes risk assessment.
  • Early identification of individuals at risk is essential for effective prevention strategies.

Purpose of the Study:

  • To develop and interpret a predictive model for prediabetes using occupational and physical activity data.
  • To identify key nonclinical predictors for early intervention and targeted prevention of prediabetes.
  • To assess the utility of machine learning algorithms in understanding lifestyle-related diabetes risk.

Main Methods:

  • Analysis of data from 33,265 participants in the Korea National Health and Nutrition Examination Survey.
  • Application of logistic regression, decision tree, XGBoost, and random forest algorithms for model development.
  • Utilizing SHapley Additive exPlanations (SHAP) for transparent interpretation of model predictors.

Main Results:

  • The random forest model achieved the highest predictive accuracy (0.8037) and AUC (0.8597).
  • Professional occupations, reduced working hours, and more walking days were linked to lower prediabetes risk.
  • Conversely, prolonged working hours and high physical job demands were associated with an elevated risk of prediabetes.

Conclusions:

  • Occupational and physical activity factors are significant nonclinical predictors of prediabetes.
  • These findings support developing accessible, lifestyle-focused prevention strategies, especially where clinical resources are limited.
  • Emphasizing lifestyle modifications is crucial for effective diabetes risk management.