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Related Experiment Video

Updated: May 16, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Predicting depression in middle-aged adults using kidney deficiency questionnaire: an exploratory machine learning

Chan-Young Kwon1, Boram Lee2, Mi Mi Ko2

  • 1Department of Oriental Neuropsychiatry, Dong-eui University College of Korean Medicine, Busan, Republic of Korea; Anti-Aging Research Center, Dong-eui University, Busan, Republic of Korea.

Explore (New York, N.Y.)
|May 14, 2026
PubMed
Summary

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Glomerular filtration rate (GFR) can be estimated from serum creatinine using the modification of diet in renal disease (MDRD) formula or the chronic kidney disease–epidemiology collaboration (CKD–EPI) equation. Both methods are widely used in clinical practice to assess kidney function and guide treatment decisions.The MDRD equation does not require weight or height measurements and is normalized to the body surface area of 1.73 m², considered the average adult surface area. This equation is...

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Korean medicine

Area of Science:

  • Gerontology
  • Psychiatry
  • Integrative Medicine

Background:

  • Korean medicine (KM) links kidney deficiency to age-related decline and depression.
  • Empirical validation of this link in modern clinical settings is limited.

Purpose of the Study:

  • To develop and validate a machine learning model for predicting depression in middle-aged adults.
  • To integrate kidney deficiency assessment with psychosocial factors for enhanced prediction.

Main Methods:

  • Analysis of baseline data from 1000 adults aged 50-65.
  • Utilized Geriatric Depression Scale Short Form (GDS-SF-K) for depression assessment.
  • Employed machine learning (multilayer perceptron) with kidney deficiency questionnaire (KDQ), social support, and BMI as predictors.
  • Model validation using 5-fold cross-validation and an independent test set.
Keywords:
Artificial intelligenceDepressionKidney deficiencyKorean medicineSocial support

Related Experiment Videos

Last Updated: May 16, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Main Results:

  • 16.4% of participants met depression criteria.
  • The optimal multilayer perceptron model incorporated KDQ score, social support, and BMI.
  • The model achieved an ROC-AUC of 0.820 and PR-AUC of 0.521 on the test set, with 67.3% sensitivity and 75.7% specificity.

Conclusions:

  • Integrating kidney deficiency assessment with psychosocial factors offers a moderately accurate, parsimonious model for depression prediction.
  • This study provides quantitative evidence for the predictive utility of the KM kidney deficiency construct.
  • Integrative approaches can improve depression risk stratification in middle-aged adults.