Hospital Length of Stay Prediction based on Patient Examination Using General features

  • Rabiatul Adawiyah Politeknik Elektronika Negeri Surabaya, Indonesia
  • Tessy Badriyah Politeknik Elektronika Negeri Surabaya, Indonesia
  • Iwan Syarif Politeknik Elektronika Negeri Surabaya, Indonesia
Keywords: Predict, length of stay, Neural Network, Data, Hospital


As of the year 2020, Indonesia has the fourth most populous country in the world. With Indonesia’s population expected to continuously grow, the increase in provision of healthcare needs to match its steady population growth. Hospitals are central in providing healthcare to the general masses, especially for patients requiring medical attention for an extended period of time. Length of Stay (LOS), or inpatient treatment, covers various treatments that are offered by hospitals, such as medical examination, diagnosis, treatment, and rehabilitation. Generally, hospitals determine the LOS by calculating the difference between the number of admissions and the number of discharges. However, this procedure is shown to be unproductive for some hospitals. A cost-effective way to improve the productivity of hospital is to utilize Information Technology (IT).  In this paper, we create a system for predicting LOS using Neural Network (NN) using a sample of 3055 subjects, consisting of 30 input attributes and 1 output attribute. The NN default parameter experiment and parameter optimization with grid search as well as random search were carried out. Our results show that parameter optimization using the grid search technique give the highest performance results with an accuracy of 94.7403% on parameters with a value of Epoch 50, hidden unit 52, batch size 4000, Adam optimizer, and linear activation. Our designated system can be utilised by hospitals in improving their effectiveness and efficiency, owing to better prediction of LOS and better visualization of LOS done by web visualization.


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M. A. Umar, “Bonus Demografi Sebagai Peluang Dan Tantangan Di Era Otonomi Daerah,” Genta Mulia, vol. 8, no. 2, pp. 90–99, 2020.

Badan Pusat Statistik, “Badan Pusat Statistik.” pp. 335–58, 2017, doi: 10.1055/s-2008-1040325. DOI:

W. M. Baihaqi, M. Dianingrum, and K. A. N. Ramadhan, “Regresi Linier Sederhana Untuk Memprediksi Kunjungan Pasien di Rumah Sakit Berdasarkan Jenis Layanan dan Umur Pasien,” J. SIMETRIS, vol. 10, no. 2, pp. 671–680, 2019.

Menteri Kesehatan, “BAB II TINJAUAN PUSTAKA,” Keputusan Menteri Kesehat., vol. 18, pp. 19–28, 2003.

A. Cedars, L. Benjamin, S. V. Burns, E. Novak, and A. Amin, “Clinical predictors of length of stay in adults with congenital heart disease,” Heart, vol. 103, no. 16, pp. 1258–1263, 2017, doi: 10.1136/heartjnl-2016-310841. DOI:

C. S. Yang, C. P. Wei, C. C. Yuan, and J. Y. Schoung, “Predicting the length of hospital stay of burn patients: Comparisons of prediction accuracy among different clinical stages,” Decis. Support Syst., vol. 50, no. 1, pp. 325–335, 2010, doi: 10.1016/j.dss.2010.09.001. DOI:

P. F. J. Tsai et al., “Length of Hospital Stay Prediction at the Admission Stage for Cardiology Patients Using Artificial Neural Network,” J. Healthc. Eng., vol. 2016, 2016, doi: 10.1155/2016/7035463. DOI:

C. Gholipour, F. Rahim, A. Fakhree, and B. Ziapour, “Using an artificial neural networks (ANNS) model for prediction of intensive care unit (ICU) outcome and length of stay at hospital in traumatic patients,” J. Clin. Diagnostic Res., vol. 9, no. 4, pp. 19–23, 2015, doi: 10.7860/JCDR/2015/9467.5828. DOI:

N. Caetano, P. Cortez, and R. M. S. Laureano, “Using data mining for prediction of hospital length of stay: An application of the CRISP-DM methodology,” Lect. Notes Bus. Inf. Process., vol. 227, pp. 149–166, 2015, doi: 10.1007/978-3-319-22348-3_9. DOI:

E. Kutafina, I. Bechtold, K. Kabino, and S. M. Jonas, “Recursive neural networks in hospital bed occupancy forecasting,” vol. 1, pp. 1–10, 2019. DOI:

A. Yin, “Predicting Hospital Length of Stay Using Multilayer Perceptron Neural Networks Abstract,” no. August, 2019.

Y. Freund and R. E. Schapire, “Experiments with a New Boosting Algorithm,” Proc. 13th Int. Conf. Mach. Learn., pp. 148–156, 1996, doi:

D. Fransiska Amalia Kurniawan, “Analisis dan Implementasi Random Forest dan Regression Tree (CART) Untuk Klasifikasi pada Misuse Intrussion Detection System,” Fak. Tek. Inform., no. Data Mining, pp. 1–7, 2011.

V. Kotu and B. Deshpande, “Model Evaluation,” Predict. Anal. Data Min., pp. 257–273, 2015, doi: 10.1016/b978-0-12-801460-8.00008-2. DOI:

How to Cite
Rabiatul Adawiyah, Badriyah, T., & Syarif, I. (2021). Hospital Length of Stay Prediction based on Patient Examination Using General features. EMITTER International Journal of Engineering Technology, 9(1), 169-181.