Machine learning models for predicting depression in Korean young employees

Computer Science

• Learning System 100%
• Machine Learning 100%
• Random Decision Forest 75%
• Sparse Logistic Regression 75%
• Support Vector Machine 50%
• Protective Factor 50%
• Online Survey 25%
• Prediction Accuracy 25%
• Machine Learning Algorithm 25%
• Predictor Variable 25%
• Related Factor 25%
• Dependent Variable 25%
• Depressive Symptom 25%
• Logistics Support 25%
• Psychosocial Risk Factor 25%

Nursing and Health Professions

• Random Forest 100%
• Logistic Regression Analysis 100%
• Area under the Curve 33%
• Depressive Disorder 33%
• Statistical Analysis 33%
• Sensitivity and Specificity 33%
• Dependent Variable 33%
• Healthcare System 33%
• Predictor Variable 33%
• Patient Social Context 33%

Psychology

• Learning Model 100%
• Dependent Variable 25%
• Statistical Method 25%
• Learning Algorithm 25%
• Depressive Disorder 25%