biostudies-literatureUnknown12(1)Alsinglawi BThis work introduces a predictive Length of Stay (LOS) framework for lung cancer patients using machine learning (ML) models. The framework proposed to deal with imbalanced datasets for classification-based approaches using electronic healthcare records (EHR). We have utilized supervised ML methods to predict lung cancer inpatients LOS during ICU hospitalization using the MIMIC-III dataset. Random Forest (RF) Model outperformed other models and achieved predicted results during the three framework phases. With clinical significance features selection, over-sampling methods (SMOTE and ADASYN) achieved the highest AUC results (98% with CI 95%: 95.3-100%, and 100% respectively). The combination of Over-sampling and under-sampling achieved the second-highest AUC results (98%, with CI 95%: 95.3Scientific reports607https://www.ebi.ac.uk/biostudies/studies/S-EPMC8755804biostudies-literatureAn explainable machine learning framework for lung cancer hospital length of stay prediction.PMC8755804Alsinglawi BAlnajjar FMubin OAlorjani MNovoa MAlshari ODarwish OfalseAn explainable machine learning framework for lung cancer hospital length of stay prediction.This work introduces a predictive Length of Stay (LOS) framework for lung cancer patients using machine learning (ML) models. The framework proposed to deal with imbalanced datasets for classification-based approaches using electronic healthcare records (EHR). We have utilized supervised ML methods to predict lung cancer inpatients LOS during ICU hospitalization using the MIMIC-III dataset. Random Forest (RF) Model outperformed other models and achieved predicted results during the three framework phases. With clinical significance features selection, over-sampling methods (SMOTE and ADASYN) achieved the highest AUC results (98% with CI 95%: 95.3-100%, and 100% respectively). The combination of Over-sampling and under-sampling achieved the second-highest AUC results (98%, with CI 95%: 95.32022-01-01T00:00:00Z2022 Jan2026-07-16T16:15:50.958Z2022-02-11T15:16:51.037ZS-EPMC87558043502251210.1038/s41598-021-04608-7