  Introduction: One of the most important tasks in the Emergency Department
(ED) is to promptly identify the patients who will benefit from hospital
admission. Machine Learning (ML) techniques show promise as diagnostic aids in
healthcare. Material and methods: We investigated the following features
seeking to investigate their performance in predicting hospital admission:
serum levels of Urea, Creatinine, Lactate Dehydrogenase, Creatine Kinase,
C-Reactive Protein, Complete Blood Count with differential, Activated Partial
Thromboplastin Time, D Dimer, International Normalized Ratio, age, gender,
triage disposition to ED unit and ambulance utilization. A total of 3,204 ED
visits were analyzed. Results: The proposed algorithms generated models which
demonstrated acceptable performance in predicting hospital admission of ED
patients. The range of F-measure and ROC Area values of all eight evaluated
algorithms were [0.679-0.708] and [0.734-0.774], respectively. Discussion: The
main advantages of this tool include easy access, availability, yes/no result,
and low cost. The clinical implications of our approach might facilitate a
shift from traditional clinical decision-making to a more sophisticated model.
Conclusion: Developing robust prognostic models with the utilization of common
biomarkers is a project that might shape the future of emergency medicine. Our
findings warrant confirmation with implementation in pragmatic ED trials.
