Assessing Readmission Rates in a Sharjah Healthcare Facility

Mohamad Alnajar, Yara Aljabi, Ayman Alzaatreh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The healthcare industry is one of the most sensitive industries as it deals with patients' health. Machine Learning techniques have been implemented to assess the performance of such industries and further improve the allocation of their resources. Many measures of performance exist to infer how a healthcare facility uses its resources. Readmission rate is a very popular rate in analyzing the performance of a healthcare facility. In this paper, we assess the readmission rate of a Sharjah healthcare facility in the first ten months of 2021. We have used classification techniques such as Logistic Regression, Random Forests, Neural Networks, and Gradient Boosting to find the best prediction model. We then used logistic regression to infer the relationships between the most important variables and the readmission rate. Results showed that the readmission rate was most influenced by the hospital departments, insurance type, marital status, age, and diastolic blood pressure. Relationships of such variables are outlined in the paper and can be further investigated to reduce readmission rates for cost reduction.

Original languageEnglish
Title of host publication2022 Advances in Science and Engineering Technology International Conferences, ASET 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665418010
DOIs
Publication statusPublished - 2022
Externally publishedYes
Event2022 Advances in Science and Engineering Technology International Conferences, ASET 2022 - Dubai, United Arab Emirates
Duration: Feb 21 2022Feb 24 2022

Publication series

Name2022 Advances in Science and Engineering Technology International Conferences, ASET 2022

Conference

Conference2022 Advances in Science and Engineering Technology International Conferences, ASET 2022
Country/TerritoryUnited Arab Emirates
CityDubai
Period2/21/222/24/22

Keywords

  • Gradient Boosting
  • Healthcare Facility
  • Logistic Regression
  • Neural Networks
  • Random Forests
  • Readmission Rate

ASJC Scopus subject areas

  • Process Chemistry and Technology
  • Artificial Intelligence
  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering
  • Mechanical Engineering
  • Waste Management and Disposal

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