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Area of Science:

  • Health Informatics
  • Preventive Medicine
  • Machine Learning in Healthcare

Background:

  • Healthcare is shifting towards community-based promotion and personalized preventive strategies.
  • Individual health risk assessments (HRAs) are key, but often rely on costly data acquisition.
  • There is a need for cost-effective methods to predict adverse health events.

Purpose of the Study:

  • To assess the feasibility of predicting mortality and chronic diseases using low-cost data.
  • To evaluate the incremental predictive value of demographic, lifestyle, family history, and personal health device data.
  • To determine if these data can replace or supplement expensive laboratory data in HRAs.

Main Methods:

  • Utilized machine learning to develop risk prediction models.
  • Employed data from the Korean National Health Insurance Service (NHIS) (2002-2019).
  • Compared model performance using different data categories (demographic, lifestyle, device, lab data).

Main Results:

  • Models using demographic, lifestyle, family history, and personal health device data showed comparable predictive performance to those including laboratory data.
  • Feature importance analysis highlighted modifiable lifestyle factors as key predictors when lab data was excluded.
  • The findings support the practicality of precise HRAs without laboratory tests.

Conclusions:

  • Accurate HRAs can be achieved using readily available data (demographic, lifestyle, family history, personal health devices).
  • This approach reduces barriers to HRAs, especially for healthy individuals, by removing the need for costly lab tests.
  • Enables more accessible and practical preventive health management strategies.