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

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

Abstract

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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Published
2021-06-26
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. https://doi.org/10.24003/emitter.v9i1.609
Section
Articles