Objective      To build a model to estimate glomerular filtration rate for Chinese patients with chronic kidney disease based on serum physiological parameters and demographic characteristics.       Methods      Generalized regression neural network (GRNN)

one approach of artificial neural network

was applied to build the model based on 562 training data set

and the performance of the model was validated in 269 validation data set. Then the results were compared with empirical equations derived from traditional regression method of statistics.       Results      Compared with empirical equations

the performance of artificial neural network was better in accuracy with statistically significant differences (P0.05).       Conclusion      Our study indicated that approach of artificial neural network

as a common machine learning method

is superior to the traditional statistical method in biomedical information processing. Our model to estimate glomerular filtration rate is more accurate.