Occupational and socioeconomic predictors of myocardial infarction and coronary heart disease: a machine learning analysis
Jihyun Noh, Yonghwan Kim, Chaeseong Lim, Su-Yeon Cho, Yujin Kwon, Eun-Soo Lee, Won Kyu Kim, Yun Hak Kim, Kihun Kim · 2026
Read Occupational and socioeconomic predictors of myocardial infarction and coronary heart disease: a machine learning analysis by Jihyun Noh, Yonghwan Kim, Chaeseong Lim, Su-Yeon Cho, Yujin Kwon, Eun-Soo Lee, Won Kyu Kim, Yun Hak Kim, Kihun Kim (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13326371.txt.
Description
Jihyun Noh and co-authors use machine learning to sort heart cases in survey data in this 2026 study. Heart disease is a leading cause of death, and most risk models rest on medical facts alone. Job and social traits are now seen as part of the picture, so the team added those traits.
They used a large United States health survey from 2023. Their aim was to classify people who already had a heart attack or heart disease, not to predict future cases. Four learning methods were tested, and one tree-based method did best. To guard against cheating from facts known only after diagnosis, they built a second model that left those facts out.
Catalog record
| CREATOR | Jihyun Noh, Yonghwan Kim, Chaeseong Lim, Su-Yeon Cho, Yujin Kwon, Eun-Soo Lee, Won Kyu Kim, Yun Hak Kim, Kihun Kim |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 38 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |