Development and evaluation of a machine learning based risk prediction model for osteoporosis
Lin Zhou, Qi Zhang, Haijing Wei, Jie Liu, Yongchang Yang · 2026
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Description
Lin Zhou and four colleagues build a machine learning model to flag patients at risk of osteoporosis, a disease that weakens bones and raises fracture risk. Their data came from 187 patients treated at a Beijing military hospital between January 2023 and September 2024. The team measured sixteen immune signals in blood, called cytokines, along with other routine markers. Eleven different learning models were trained and compared.
Sixteen features, including the cytokine IL-8, were kept as risk markers for the final work. A gradient boosting model performed best, with strong accuracy and a fair balance between catching cases and avoiding false alarms. A tool called SHAP was used to show which features pushed each prediction up or down. The authors say such a model could help doctors spot high-risk patients earlier, before fractures occur.
Catalog record
| CREATOR | Lin Zhou, Qi Zhang, Haijing Wei, Jie Liu, Yongchang Yang |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 27 KB |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |