Univrsity Of Tehran Press Journal of Information Technology Management 2980-7972 18 4 2026 10 01 A Smart Healthcare Management System for Predicting ICU Length of Stay Using Machine Learning and MIMIC-IV 134 149 108919 10.22059/jitm.2026.108919 EN Danish Ather Professor, Department of Computer Science and Engineering, Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Tashkent 100084, Uzbekistan. Dharm Raj Assistant Professor, Department of Computer Science and Engineering, Sharda School of Engineering & Technology, Sharda University, Greater Noida, Uttar Pradesh 201306, India. Suhail Javed Quraishi Assistant Professor, Manav Rachna International Institute of Research and Studies, Sector 43, Faridabad, Haryana 121004, India. Sonal Pathak Assistant Professor, Manav Rachna International Institute of Research and Studies, Sector 43, Faridabad, Haryana 121004, India. Manik Rakhra Assistant Professor, School of Computer Science and Engineering, Lovely Professional University, Phagwara, India. 0000-0003-1680-6992 Kunchanapaalli Rama Krishna Professor, Department of Computer Science and Information Technology, K L Deemed to be University, Vaddeswaram 522502, Andhra Pradesh, India. Journal Article Stays in ICUs are expensive to accommodate and difficult to forecast, yet even a simple forecast can be helpful, as it enables ICUs to allocate beds and staff and schedule transfer times. We address a particular problem of this kind: how much of the length of stay can be determined based on the first day of hospitalization? Our dataset is the publicly available MIMIC-IV Clinical Database Demo, from which we use 140 hospitalization records, retain the 117 that lasted at least one day, and derive 47 features from the first 24 hours of data on vital signs, medication and fluid intake, urine volume, and bedside procedures. Both formulations are addressed for the same set of patients. Exact forecasting of duration turned out to be rather complicated in our case: despite selecting the best-performing regression technique (random forest), the resulting mean absolute error was 2.85 days, barely outperforming the mean baseline predictor and producing a negative value for the coefficient of determination due to a few very long stays contributing disproportionately to the squared error. Yet, what has been accomplished is the ranking, where the Spearman rank correlation. https://jitm.ut.ac.ir/article_108919_76cccc64cad9cdbd33256bf361668073.pdf