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A Taylor plot of the RBP prediction model.
Abstract
This study combined geographic factors to predict Chinese healthy male RBP reference values from a geographic perspective, with the aim of exploring the spatial distribution and regional differences in Chinese healthy male Retinol-Binding Protein(RBP) reference values, and then providing a theoretical basis for medical diagnosis of healthy male RBP reference values in different regions of China. Using the actual measured RBP values of 24,502 healthy men in 256 cities in China combined with 16 geographical factors as the base data, the spatial autocorrelation, correlation analysis and support vector machine were used to predict the RBP reference values of healthy men in 2322 cities in China, and to generate a spatial distribution map of the RBP reference values of healthy men in China. It was found that the spatial distribution of healthy male RBP reference values in China showed a trend of gradual increase from the first to the third terrain steps. Combined with the distribution map, it is suggested that the RBP reference values of healthy men in China should be divided into the low value zone of the first-level terrain step (25mg/L~40mg/L), the middle value zone of the second-level terrain step (40mg/L~45mg/L) and the high value zone of the third-level terrain step (45mg/L~52mg/L).</div- Image
- Figure
- Evolutionary Biology
- Ecology
- Environmental Sciences not elsewhere classified
- Biological Sciences not elsewhere classified
- support vector machine
- middle value zone
- low value zone
- level terrain step
- high value zone
- 16 geographical factors
- third terrain steps
- 502 healthy men
- spatial distribution map
- rbp reference values
- reference values
- healthy men
- distribution map
- spatial distribution
- geographical environment
- spatial autocorrelation
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- theoretical basis
- regional differences
- medical diagnosis
- gradual increase
- geographic perspective
- different regions
- correlation analysis
- binding protein
- base data
- 256 cities
- 2322 cities