Development and Comparative Evaluation of Machine Learning Models using Clinically Relevant Features for Predicting Newborn Patients’ Length of Stay
DOI:
https://doi.org/10.52436/1.jutif.2026.7.3.5410Keywords:
length of stay, machine learning, newborns, prediction, Random ForestAbstract
The Length of Stay (LOS) of newborns is a crucial indicator for healthcare management and hospital resource allocation. However, prior research has yet to systematically compare machine learning models for newborn LOS prediction using clinically pertinent features in developing-country hospital contexts, creating an important methodological and contextual gap. Accurate prediction of LOS is urgently needed to support timely clinical decision-making and prevent overcrowding, inefficiencies, and unnecessary healthcare costs. This study aims to identify factors influencing LOS and develop a predictive model for newborn LOS using several machine learning algorithms. A comparison was conducted among Linear Regression, Random Forest Regression, Support Vector Regression (SVR), and Artificial Neural Networks (ANN). The dataset consisted of medical records of newborn patients from three private hospitals in Indonesia. The research included data collection and understanding, data preprocessing, modeling, and evaluation. Experimental results show that Random Forest Regression achieved the best predictive performance, with MAE = 0.019, MSE = 0.011, RMSE = 0.086, and R² = 0.987. Feature importance analysis revealed that gender, referral source, insurance type, and diagnosis were the most influential predictors of LOS. This study contributes to the advancement of machine learning applications in healthcare data analytics and provides evidence-based insights to support neonatal care planning and hospital resource optimization.
Downloads
References
WHO, “Newborn Mortality,” WHO. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/levels-and-trends-in-child-mortality-report-2021
Y. G. Robi and T. M. Sitote, “Neonatal Disease Prediction Using Machine Learning Techniques,” J. Healthc. Eng., vol. 2023, no. 1, pp. 1–16, 2023, doi: 10.1155/2023/3567194.
G. Almeida, F. B. Correia, and A. R. Borges, “Hospital Length-of-Stay Prediction Using Machine Learning Algorithms — A Literature Review,” Apllied Sci., vol. 2024, no. 14, pp. 1–20, 2024, doi: https://www.mdpi.com/2076-3417/14/22/10523.
L. Garg et al., “Characterising Hospital Admission Patterns and Length of Stay in the Emergency Department at Mater Dei Hospital Malta,” Preprints, vol. 1, no. February, pp. 28–30, 2023, doi: 10.20944/preprints202302.0315.v1.
K. Stone, R. Zwiggelaar, P. Jones, N. Mac Parthaláin, and D. Yoon, “A Systematic Review of The Prediction of Hospital Length of Stay: Towards a Unified Framework,” PLOS Ddigital Heal., vol. 1, no. 4, pp. 1–39, 2022, doi: 10.1371/journal.pdig.0000017.
M. A. Shbool et al., “Machine Learning Approaches to Predict Patient’s Length of Stay in Emergency Department,” Appl. Comput. Intell. Soft Comput., vol. 2023, no. 1, pp. 1–13, 2023, doi: 10.1155/2023/8063846.
S. G. Gurazada, S. Gao, F. Burstein, and P. Buntine, “Predicting Patient Length of Stay in Australian Emergency Departments Using Data Mining,” Sensors, vol. 22, no. 13, pp. 1–15, 2022, doi: https://www.mdpi.com/1424-8220/22/13/4968.
J. Chrusciel, F. Girardon, L. Roquette, D. Laplanche, and A. Duclos, “The Prediction of Hospital Length of Stay using Unstructured Data,” BMC Med. Inform. Decis. Mak., vol. 4, pp. 1–9, 2021, doi: 10.1186/s12911-021-01722-4.
É. Arnaud et al., “Development and Clinical Interpretation of an Explainable AI Model for Predicting Patient Pathways in the Emergency Department : A Retrospective Study,” Apllied Sci., vol. 15, no. 2025, pp. 1–22, 2025, doi: https://doi.org/10.3390/app15158449.
Y. Liu et al., “Evaluation of the Need for Intensive Care in Children With Pneumonia : Machine Learning Approach Corresponding,” JMIR Med. INFORMATICS, vol. 10, no. 1, pp. 1–11, 2022, doi: 10.2196/28934.
J. Wu et al., “Development of A Scoring Tool for Predicting Prolonged Length of Hospital Stay in Peritoneal Dialysis Patients Through Data Mining,” Ann. Transl. Med., vol. 8, no. 21, pp. 1–15, 2020, doi: 10.21037/atm-20-1006.
A. J. Zeleke, P. Palumbo, P. Tubertini, R. Miglio, and L. Chiari, “Machine Learning-Based Prediction of Hospital Prolonged Length of Stay Admission at Emergency Department : a Gradient Boosting Algorithm Analysis,” Front. Artif. Intell., vol. 6, no. 2023, pp. 1–19, 2023, doi: 10.3389/frai.2023.1179226.
X. Zeng, “Length of Stay Prediction Model of Indoor Patients Based on Light Gradient Boosting Machine,” Comput. Intell. Neurosci., vol. 2022, no. 1, pp. 1–14, 2022, doi: https://doi.org/10.1155/2022/9517029 Research.
L. Chen and H. B. Klasky, “Six Machine-Learning Methods for Predicting Hospital-Stay Duration for Patients with Sepsis : A Comparative Study,” SoutheastCon 2022, vol. 2022, pp. 302–309, 2022, doi: 10.1109/SoutheastCon48659.2022.9764052.
S. Al-Gahtani and M. M. Shoukri, “Analysis of Length of Stay ( LOS) Data from the Medical Records of Tertiary Care Hospital in Saudi Arabia for Five Diagnosis Related Groups : Application of Cox Prediction Model,” Open J. Stat., vol. 11, pp. 99–112, 2021, doi: 10.4236/ojs.2021.111005.
A. Massahiro et al., “The Evolution of CRISP-DM for Data Science : Methods , Processes and Frameworks,” SBC Rev. Comput. Sci., vol. 2023, no. 4, pp. 1–16, 2024, doi: 10.5753/reviews.2024.3757.
Y. Taules, S. Gros, M. Viladrosa, N. Llorens, S. Solis, and O. Yuguero, “Use of Artificial Intelligence for Reverse Referral Between A Hospital Emergency Department and A Primary Urgent Care Center,” Front. Digit. Heal., vol. 7, no. March, pp. 5–10, 2025, doi: 10.3389/fdgth.2025.1546467.
A. I. Taloba, R. M. A. El-aziz, H. M. Alshanbari, and A.-A. H. El-Bagoury, “Estimation and Prediction of Hospitalization and Medical Care Costs Using Regression in Machine Learning,” J. Healthc. Eng., vol. 2022, no. 1, pp. 1–10, 2022, doi: https://doi.org/10.1155/2022/7969220 Research.
S. Melhem, A. Al-Aiad, and M. S. Al-Ayyad, “Patient Care Classification Using Machine Learning Techniques,” in International Conference on Information and Communication Systems (ICICS), 2021, pp. 1–6. doi: 10.1109/ICICS52457.2021.9464582.
F. Chollet, Deep Learning with Python. United States: Manning Publications Co, 2018. doi: 10.4018/978-1-7998-3095-5.ch003.
A. Masbakhah, U. Sa’adah, and M. Muslikh, “Heart Disease Classification Using Random Forest and Fox Algorithm as Hyperparameter Tuning,” J. Electron. Electromed. Eng. Med. Informatics, vol. 7, no. 4, pp. 964–976, 2025, doi: 10.35882/jeeemi.v7i4.932.
M. Awad and R. Khanna, Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers, no. April 2015. 2015. doi: 10.1007/978-1-4302-5990-9.
H. Huang, R. Jia, J. Liang, J. Dang, and Z. Wang, “Wind Power Deterministic Prediction and Uncertainty Quantification Based on Interval Estimation,” J. Sol. Energy Eng. Trans. ASME, vol. 143, no. 6, pp. 1–12, 2021, doi: 10.1115/1.4051430.
L. Costa et al., “Multilayer Perceptron,” in Introduction to Computational Intelligence, no. January, IEEE, 2023, pp. 1–6. doi: 10.1145/3090354.3090427.
H. Yin, “Enhancing Directional Accuracy in Stock Closing Price Value Prediction Using a Direction-Integrated MSE Loss Function,” in Proceedings ofthe 1st International Conference on Data Analysis and Machine Learning (DAML 2023), 2023, pp. 119–126. doi: 10.5220/0012810200003885.
T. O. Hodson, “Root-Mean-Square Error (RMSE) or Mean Absolute Error (MAE): When to Use Them or Tot,” Geosci. Model Dev., vol. 15, no. 14, pp. 5481–5487, 2022, doi: 10.5194/gmd-15-5481-2022.
A. Danujan, T. Nithusikan, Athauda, Hearth, and Senanayake, “Predicting Inpatient Bed Demand Using Machine Learning,” in Young Members’ Technical Conference 2024, 2023, pp. 150–157. [Online]. Available: https://www.researchgate.net/publication/391584272_Predicting_Inpatient_Bed_Demand_Using_Machine_Learning
B. Panay, N. Baloian, J. A. Pino, S. Peñafiel, H. Sanson, and N. Bersano, “Predicting Health Care Costs Using Evidence Regression,” in MDPI, 2019, pp. 1–13. doi: 10.3390/proceedings2019031074.
K. Alghatani, N. Ammar, A. Rezgui, and A. Shaban-nejad, “Predicting Intensive Care Unit Length of Stay and Mortality Using Patient Vital Signs: Machine Learning Model Development and Validation,” JMIR Med. INFORMATICS, vol. 9, no. 5, pp. 1–23, 2021, doi: 10.2196/21347.
J. An, M. Jung, S. Ryu, Y. Choi, and J. Kim, “Analysis of Length of Stay for Patients Admitted to Korean Hospitals Based on The Korean National Health Insurance Service Database,” Informatics Med. Unlocked, vol. 37, no. October 2022, p. 101178, 2023, doi: 10.1016/j.imu.2023.101178.
D. Bertsimas, J. Pauphilet, J. Stevens, and M. Tandon, “Predicting Inpatient Flow at A Major Hospital using Interpretable Analytics,” Manuf. Serv. Oper. Manag., vol. 2020, no. 1, pp. 1–42, 2020, doi: https://doi.org/10.1101/2020.05.12.20098848.
Additional Files
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Gandung Triyono, Billy Marentek, Mohammad Syafrullah
This work is licensed under a Creative Commons Attribution 4.0 International License.
