Zhanbing Ma
Biographic Data
| ID | 7186573 |
|---|---|
| NAME | Zhanbing Ma |
| GIVEN NAMES | Zhanbing |
| FAMILY NAME | Ma |
| SIGNATURE | MA Z |
| AFFILIATIONS | Ningxia Medical University |
| ORCID | 0000-0002-3001-2980 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2018 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Interpretable machine learning method to predict the risk of pre-diabetes using a national-wide cross-sectional data
The constructed model comprises nine easily accessible predictive factors, which prove highly effective in forecasting the risk of pre-diabetes. Concurrently, we have quantified the specific impact of each predictive factor on the risk and ranked them based on their influence. This result may serve as a convenient tool for early identification of individuals at high risk of pre-diabetes, providing effective guidance for preventing the progression…
Digit ratio (2D
OBJECTIVES: Digit ratio, especially the second-to-fourth digit ratio (2D:4D), is a proxy indicator for prenatal exposure and sensitivity to sexual hormones which may influence the susceptibility to certain cancers. The aim of the present study was to investigate whether there is a possible association between 2D:4D and gastric cancer (GCA) in north Chinese women. METHODS: Photographs of the left and right hands of 167 women (controls: 113; patien…
No prominent works on this page.
Digit ratio (2D
OBJECTIVES: Digit ratio, especially the second-to-fourth digit ratio (2D:4D), is a proxy indicator for prenatal exposure and sensitivity to sexual hormones which may influence the susceptibility to certain cancers. The aim of the present study was to investigate whether there is a possible association between 2D:4D and gastric cancer (GCA) in north Chinese women. METHODS: Photographs of the left and right hands of 167 women (controls: 113; patien…
Interpretable machine learning method to predict the risk of pre-diabetes using a national-wide cross-sectional data
The constructed model comprises nine easily accessible predictive factors, which prove highly effective in forecasting the risk of pre-diabetes. Concurrently, we have quantified the specific impact of each predictive factor on the risk and ranked them based on their influence. This result may serve as a convenient tool for early identification of individuals at high risk of pre-diabetes, providing effective guidance for preventing the progression…
Medicine (2 works) · Artificial Intelligence (1 works) · Artificial Intelligence in Healthcare (1 works) · Artificial neural network (1 works) · Cancer (1 works) · Computer Science (1 works) · Congenital heart defects research (1 works) · Diabetes mellitus (1 works) · Diabetes, Cardiovascular Risks, and Lipoproteins (1 works) · Digit ratio (1 works)