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A feature optimization study based on a diabetes risk questionnaire.

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Obesity is the leading diabetes risk factor, followed by psychological issues, age, and high blood pressure. Optimizing questionnaires improves diabetes prevention efficiency and public awareness.

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diabetesdiabetes risk questionnairefeature enumerationmachine learningpublic healthrisk prediction

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Area of Science:

  • Public Health
  • Epidemiology
  • Biostatistics

Background:

  • Diabetes prevalence is increasing globally, creating significant societal and individual burdens.
  • Effective diabetes risk prediction requires optimized questionnaires and variable selection.
  • Raising public awareness of diabetes risk is crucial for prevention efforts.

Purpose of the Study:

  • To enhance diabetes risk prediction questionnaire effectiveness.
  • To optimize the selection of characteristic variables for diabetes risk assessment.
  • To increase resident awareness of diabetes risk factors.

Main Methods:

  • Utilized survey data from the US Centers for Disease Control and Prevention's risk factor monitoring system.
  • Applied univariate analysis, data preprocessing (standardization, SMOTE), and machine learning (ML) techniques.
  • Employed enumerated feature variables to evaluate correlations among diabetes risk factors.

Main Results:

  • Obesity identified as the most significant diabetes risk factor.
  • Other key factors include psychological elements, advanced age, high cholesterol, and hypertension.
  • Correlations were also observed for alcohol abuse, cardiovascular disease history, mobility issues, and low income.

Conclusions:

  • Optimizing questionnaire variables and length enhances efficiency for diabetes prevention and follow-up.
  • The study's methodology provides a framework for analyzing risk correlations in other diseases.
  • Findings contribute to increased societal awareness of high-risk populations for diabetes.