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Developing a Predictive Model for Depressive Disorders Using Stacking Ensemble and Naive Bayesian Nomogram: Using

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Summary
This summary is machine-generated.

This study identified key factors contributing to depression in older Korean women living alone, including stress and sleep. A predictive model was developed to aid physicians in identifying at-risk individuals.

Keywords:
depressive disordersextreme gradient boosting (XGBoost)multiple risk factorsnaive Bayesian nomogramstacking ensemblesynthetic minority oversampling technique (SMOTE)

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Area of Science:

  • Gerontology
  • Public Health
  • Computational Medicine

Background:

  • Depression is a significant health concern among older adults, particularly those living alone.
  • Understanding risk factors is crucial for developing targeted prevention strategies in primary care.

Purpose of the Study:

  • To identify major risk factors for depressive disorders in South Korean female older adults living alone.
  • To develop a predictive nomogram for early detection of depression in primary care settings.

Main Methods:

  • Analysis of epidemiological survey data from 582 female older adults (≥60 years old).
  • Utilized stacking ensemble machine learning techniques, including Gradient Boosting Machine (GBM), Random Forest, Adaboost, Support Vector Machine (SVM), and XGBoost.
  • Developed a naive Bayesian nomogram based on identified predictors.

Main Results:

  • Six major predictors for depressive disorders were identified: stress perception, subjective health, n-6 fatty acid, n-3 fatty acid, sitting hours per day, and sleep hours per day.
  • The developed nomogram aids primary physicians in interpreting high-risk groups for depressive disorders.

Conclusions:

  • Stress perception, lifestyle factors (diet, physical activity, sleep), and subjective health are significant predictors of depression in this demographic.
  • The nomogram provides a practical tool for primary care to assess depression risk in older women living alone.
  • Further research should incorporate broader measurable factors like social support.