Prediction of Return-to-original-work after an Industrial Accident Using Machine Learning and Comparison of Techniques
Journal of Korean Medical Science
;
: e144-2018.
Artigo
em Inglês
| WPRIM
| ID: wpr-714376
ABSTRACT
BACKGROUND:
Many studies have tried to develop predictors for return-to-work (RTW). However, since complex factors have been demonstrated to predict RTW, it is difficult to use them practically. This study investigated whether factors used in previous studies could predict whether an individual had returned to his/her original work by four years after termination of the worker's recovery period.METHODS:
An initial logistic regression analysis of 1,567 participants of the fourth Panel Study of Worker's Compensation Insurance yielded odds ratios. The participants were divided into two subsets, a training dataset and a test dataset. Using the training dataset, logistic regression, decision tree, random forest, and support vector machine models were established, and important variables of each model were identified. The predictive abilities of the different models were compared.RESULTS:
The analysis showed that only earned income and company-related factors significantly affected return-to-original-work (RTOW). The random forest model showed the best accuracy among the tested machine learning models; however, the difference was not prominent.CONCLUSION:
It is possible to predict a worker's probability of RTOW using machine learning techniques with moderate accuracy.
Texto completo:
DisponíveL
Índice:
WPRIM (Pacífico Ocidental)
Assunto principal:
Árvores de Decisões
/
Acidentes de Trabalho
/
Modelos Logísticos
/
Razão de Chances
/
Florestas
/
Indenização aos Trabalhadores
/
Máquina de Vetores de Suporte
/
Retorno ao Trabalho
/
Conjunto de Dados
/
Aprendizado de Máquina
Tipo de estudo:
Estudo de etiologia
/
Estudo prognóstico
/
Fatores de risco
Idioma:
Inglês
Revista:
Journal of Korean Medical Science
Ano de publicação:
2018
Tipo de documento:
Artigo
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