Optimizing Patient Length of Stay with Machine Learning: a Comparative Study Using Neural Networks Methods, Regression Methods, and Apriori Algorithm

EasyChair Preprint 15260

Abstract

The duration of an inpatient stay affects hospital administration and improves hospital effectiveness in terms of controlling expenses and raising patient standards. It also assists in identifying the correlations among illnesses requiring hospitalization. For our study, we took 24,150 records from of the Open Data database pertaining to inpatient admissions in 2023. We used a number of methods, including Neural Networks, Deep Learning, Linear Regression, and Support Vector Machines, to predict the Length of Stay (LOS). We converted the data to numerical form for predictive purposes, dividing the dataset into 70% for training and 30% for testing. We assessed the model's performance using Root Mean Squared Error (RMSE) and split the forecast into four LOS categories: 0-2, 3-4, 5-7, and 8 days or more. The study also employed the Apriori algorithm to identify illness association rules that could impact LOS estimates. The results showed that identifying illness correlations is one element that might aid in enhancing the capacity to predict LOS.

Keyphrases: Apriori algorithm, Healthcare Management, Length of Stay, Support Vector Machines, association rules, deep learning, linear regression, neural networks

@booklet{EasyChair:15260,
  author    = {Sahatas Chatnopakun and Kant Panyavanich and Maleerat Maliyaem},
  title     = {Optimizing Patient Length of Stay with Machine Learning: a Comparative Study Using Neural Networks Methods, Regression Methods, and Apriori Algorithm},
  howpublished = {EasyChair Preprint 15260},
  year      = {EasyChair, 2024}}