Earticle

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

키워드

저자

• 변해원 [ Hae-Won Byeon | Department of AI-Software, Inje University, South Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국기계항공기술학회지(구 한국기계기술학회지) [Journal of the Korean Society of Mechanical and Aviation Technology ]

• 간기

  격월간

• pISSN

  1229-604X

• eISSN

  2508-3805

• 수록기간

  1999~2026

• 등재여부

  KCI 등재

• 십진분류

  KDC 550 DDC 620