The prognosis of multiple myeloma (MM) patients was poor in white-American patients as compared to black-American patients. This study aimed to predict the death of MM patients in whites based on the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) database. A total of 28,912 white MM patients were included in this study. Data were randomly divided into a training set and a test set (7 : 3). The random forest and 5-fold cross-validation were used for developing a prediction model. The performance of the model was determined by calculating the area under the curve (AUC) with 95% confidence interval (CI). MM patients in the death group had older age, higher proportion of tumor distant metastasis, bone marrow as the disease site, receiving radiotherapy, and lower proportion of receiving chemotherapy than that in the survival group (all P < 0.001). The AUC of the random forest model in the training set and testing set was 0.741 (95% CI, 0.740-0.741) and 0.703 (95% CI, 0.703-0.704), respectively. In addition, the AUC of the age-based model was 0.688 (95% CI, 0.688-0.689) in the testing set. The results of the DeLong test indicated that the random forest model had better predictive effect than the age-based model (Z = 7.023, P < 0.001). Further validation was performed based on age and marital status. The results presented that the random forest model was robust in different age and marital status. The random forest model had a good performance to predict the death risk of MM patients in whites.
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