Improving risk prediction for depression via Elastic Net regression - Results from Korea National Health Insurance Services Data. Academic Article uri icon



  • Depression, despite its high prevalence, remains severely under-diagnosed across the healthcare system. This demands the development of data-driven approaches that can help screen patients who are at a high risk of depression. In this work, we develop depression risk prediction models that incorporate disease co-morbidities using logistic regression with Elastic Net. Using data from the one million twelve-year longitudinal cohort from Korean National Health Insurance Services (KNHIS), our model achieved an Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) of 0.7818, compared to a traditional logistic regression model without co-morbidity analysis (AUC of 0.6992). We also showed co-morbidity adjusted Odds Ratios (ORs), which may be more accurate independent estimate of each predictor variable. In conclusion, inclusion of co-morbidity analysis improved the performance of depression risk prediction models.

publication date

  • February 10, 2017



  • Comorbidity
  • Depression
  • National Health Programs


PubMed Central ID

  • PMC5333336

Scopus Document Identifier

  • 85028676770

PubMed ID

  • 28269945

Additional Document Info


  • 2016