Machine Learning in the Prediction of Costs for Liver Transplantation

Results: A total of 2,274 individual patients met our inclusion criteria, 1,090 patients for the year 2011 and 1,184 for 2012. The most important variables predicting cost and LOS were consistent across all models and included the Charlson and Van Walraven comorbidity scores. The best performing model predicting total cost was Support Vector Machine with Linear Kernel with root mean square error (RMSE) values of 0.561 whereas for LOS was the Principal Component Analysis (RMSE=0.743). When evaluating predictors of total cost and LOS, Van Walraven score >26.5 constituted cost-drivers with an average total cost of 207,041 US dollars whereas scores ranging from 21.5-26.4 were associated with a mean increase in the LOS of 26 days.