An artificial neuron network (ANN) model combining both the genetic risk factors and clinical factorsmay be effective in prediction of chemotherapy-induced adverse events. Purpose: To identify genetic factors and clinical factors associated with bone marrow suppression in cervical cancer patient, and to build a model for chemotherapy-induced neutropenia prediction. Methods: We performed a genome wide association study on a cohort to identify genetic determinants. Samples were genotyped using the Axiom CHB 1.0. The primary analyses focused on the scan of 657178 single-nucleotide polymorphisms (SNPs). Artificial neural network were used to integrating clinical factors and genetic factors to predict the occurrence of neutropenia. Results: 32 variants associated with neutropenia in the patients after chemotherapy were found (P < 1 x 10(-4)). During internal validation and external validation, artificial neural network performed well in predicting neutropenia with considerable accuracy, which is 88.9% and 81.7% respectively. ROC analysis had acceptable areas under the curve of 0.897 for the internal validation sample and 0.782 for the external validation sample. Conclusion: Neutropenia may be associated with both genetic factors and clinical factors. Our study found that the artificial neural networks model based on the multiple risk factors jointly, can effectively predict the occurring of neutropenia, which provides some guidance before the starting of chemotherapy.