Development and validation of a robust logistic regression model for predicting osteoporosis in older people
Hongxia Xue, Xuewei Hao, Dandan Liu, Yongqiang Li, Haiquan Yu · 2026
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Description
Hongxia Xue and four colleagues build, in a 2026 study, a prediction model for osteoporosis in older adults using routine health check data. The team analyzed 852 people aged sixty and over from one examination center, of whom about 29 percent had the bone-thinning disease. Nineteen candidate clues, from body mass to blood fats, entered the screen.
A selection method kept twelve features. Five algorithms then competed, from plain logistic regression to boosted trees. The fanciest models looked perfect on training data, a warning sign of overfitting rather than skill. On the held-out group, the simpler regression held up as the robust choice, matching the study's stated aim. The authors argue that honest, well-checked simple models serve clinics better than dazzling ones. Their tool aims to flag high-risk elders early, using numbers any checkup already collects.
Catalog record
| CREATOR | Hongxia Xue, Xuewei Hao, Dandan Liu, Yongqiang Li, Haiquan Yu |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 41 KB |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |