AI-Based Hematological Age Predictors and the Association Between Biological Age Acceleration and Type 2 Diabetes Mellitus — Chongqing Municipality, China, 2015–2021
Zhe Yin, Yingnan Song, Junhui Zhang, Qiaoyun Dai, Xinyuan Zhang, Xueying Yang, Na Nie, Cuixia Chen, Zongfu Cao, Xu Ma · 2024
Read AI-Based Hematological Age Predictors and the Association Between Biological Age Acceleration and Type 2 Diabetes Mellitus — Chongqing Municipality, China, 2015–2021 by Zhe Yin, Yingnan Song, Junhui Zhang, Qiaoyun Dai, Xinyuan Zhang, Xueying Yang, Na Nie, Cuixia Chen, Zongfu Cao, Xu Ma (2024) 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-11561367.txt.
Description
IntroductionBiological age (BA) can represent the actual state of human aging more accurately than chronological age (CA).MethodsUsing hematological data from 112,925 participants in southwestern China, collected between 2015 and 2021, this study constructed BA predictors using 7 machine learning (ML) methods (tailored separately for male and female populations).
Catalog record
| CREATOR | Zhe Yin, Yingnan Song, Junhui Zhang, Qiaoyun Dai, Xinyuan Zhang, Xueying Yang, Na Nie, Cuixia Chen, Zongfu Cao, Xu Ma |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 15 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |