HIGHLIGHTS
SUMMARY
This study tries to establish an ML model using the information about the diagnosis, treatment, service, and cost of individual patients to predict LOS. In the study, five ML algorithms (LR, RR, RFR, XGBR, and LightGBM) and six feature encoding methods (label encoding, count encoding, one-hot encoding, target encoding, leave-one-out encoding, and the proposed encoding method) were used and compared during the model building. Bacchi et_al proposed an artificial neural_network (ANN)-based prediction model for predicting the LOS in stroke patients. The "Length of Stay" in the dataset is the target . . .
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