원문정보
초록
영어
This study aims to predict return-to-work outcomes for workers injured in industrial accidents using a TabNet-RUSBoost hybrid model. The study analyzed data from 1,383 workers who had completed recuperation. Key predictors identified include length of recuperation, disability grade, occupation activity, self-efficacy, and socioeconomic status. The model effectively addresses class imbalance and demonstrates superior predictive performance. These findings underscore the importance of a holistic approach, incorporating both medical and psychosocial factors.
목차
ABSTRACT
1. Introduction
2. Materials and Methods
2.1 Comparative Models: Decision Tree (CART), AdaBoost, RUSBoost, and TabNet
2.2 Data Preprocessing: Handling Missing Values
2.3 Feature Engineering
2.4 Model Training: Decision Tree (CART)
2.5 AdaBoost
2.6 RUSBoost with TabNet
2.7 TabNet
2.8 Hyperparameter Tuning
2.9 Model Evaluation
2.10 Data Source
2.11 Measurement
3. Results
3.1 Model Performance
3.2 Comparative Analysis
3.3 Feature Importance
3.4 Model Rovustness and Generalizability
4. Conclusion
References
1. Introduction
2. Materials and Methods
2.1 Comparative Models: Decision Tree (CART), AdaBoost, RUSBoost, and TabNet
2.2 Data Preprocessing: Handling Missing Values
2.3 Feature Engineering
2.4 Model Training: Decision Tree (CART)
2.5 AdaBoost
2.6 RUSBoost with TabNet
2.7 TabNet
2.8 Hyperparameter Tuning
2.9 Model Evaluation
2.10 Data Source
2.11 Measurement
3. Results
3.1 Model Performance
3.2 Comparative Analysis
3.3 Feature Importance
3.4 Model Rovustness and Generalizability
4. Conclusion
References
키워드
저자정보
참고문헌
자료제공 : 네이버학술정보