Machine learning-based prediction of dissatisfaction after occupational injury: a retrospective cohort study using the nationwide Korean workers’ compensation insurance database
Youjin Lee, Taeyeon Kim, Dahyeon Koo, Dougho Park · 2025
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Description
Youjin Lee and co-authors build machine models that guess which hurt workers end up unhappy with their claim care. The study appeared in 2025. It draws on a five-year panel of Korean claim data. The group held close to twenty-three hundred workers who had closed their care.
About one in four of the workers was unhappy at the end. A plain model tied unhappiness to job loss, lack of private health cover, and low felt wealth. Four machine models were tried next to the plain one. The best machine model scored a bit higher in split skill. Trait ranks from a share tool showed which inputs drove the guesses most.
Catalog record
| CREATOR | Youjin Lee, Taeyeon Kim, Dahyeon Koo, Dougho Park |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 35 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |