Correlations of Complete Blood Count with Alanine and Aspartate Transaminase in Chinese Subjects and Prediction Based on Back-Propagation Artificial Neural Network (BP-ANN)

BACKGROUND: The complete blood count (CBC) is the most common examination used to monitor overall health in clinical practice. Whether there is a relationship between CBC indexes and alanine transaminase (ALT) and aspartate aminotransferase (AST) has been unclear. MATERIAL/METHODS: In this study, 572 normal-weight and 346 overweight Chinese subjects were recruited. The relationship between CBC indexes with ALT and AST were analyzed by Pearson and Spearman correlations according to their sex, then we conducted colinearity diagnostics and multiple linear regression (MLR) analysis. A prediction model was developed by a back-propagation artificial neural network (BP-ANN). RESULTS: ALT was related to 4 CBC indexes in the male normal-weight group and 3 CBC indexes in the female group. In the overweight group, ALT had a similar relationship with the normal group, but there was only 1 index related with AST in the normal-weight group and male overweight groups. The ALT regression models were developed in normal-weight and overweight people, which had better correlation coefficient (R>0.3). After training 1000 epochs, the BP-ANN models of ALT achieved higher correlations than MLR models in normal-weight and overweight people. CONCLUSIONS: ALT is a more suitable index than AST for developing a regression model. ALT can be predicted by CBC indexes in normal-weight and overweight individuals based on a BP-ANN model, which was better than MLR analysis.