Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)

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Machine Learning Models for Predicting Hospital Readmission Rates

Authors
D. Sravanthi1, *, C. V. P. R. Prasad2, Ankita Sharma3, S. Venkateswarlu4, J. Sasi Bhanu5, Golla Saidulu6
1Asst. Professor, CSE-DS, MLR Institute of Technology, Dundigal, Hyderabad, Telangana, India
2Professor & Dean, Department of CSE, Malla Reddy Engineering College for Women, Hyderabad, Telangana, India
3Asst. Professor, Jodhpur Institute of Engineering & Technology, Nh-62, Pali Road, Mogra, Jodhpur, Rajasthan, India
4Principal, Narasaraopeta Engineering College, Narasaraopeta, Andhra Pradesh, India
5Professor, CMR College of Engineering and Technology, Kandlakoya, Hyderabad, Telangana, India
6Professor, CMR College of Engineering and Technology, Kandlakoya, Hyderabad, Telangana, India
*Corresponding author. Email: sravanthireddyen@gmail.com
Corresponding Author
D. Sravanthi
Available Online 23 May 2025.
DOI
10.2991/978-94-6463-718-2_108How to use a DOI?
Keywords
Hospital readmissions; machine learning; predictive models; logistic regression; neural networks; decision trees; healthcare analytics
Abstract

Increasing hospital readmissions are a major issue in healthcare management worldwide, and from the financial and clinical points of view. Exact estimation of readmission rates of hospitals helps in studying the outcomes of interventional and allocating resources. This paper evaluates various models for predicting readmission rates of hospitals considering the level accuracy, model interpretability and practice to be used. Logistic regression, decision tree, random forest and neural network models are tested on a public data set. Their findings state that a higher overall accuracy is achieved by these complex neural networks at the cost of interpretability which is an important case especially in the areas of healthcare. Instead, on the basis of various contexts of healthcare systems, some suggestions for model selection are provided.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
Series
Advances in Computer Science Research
Publication Date
23 May 2025
ISBN
978-94-6463-718-2
ISSN
2352-538X
DOI
10.2991/978-94-6463-718-2_108How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - D. Sravanthi
AU  - C. V. P. R. Prasad
AU  - Ankita Sharma
AU  - S. Venkateswarlu
AU  - J. Sasi Bhanu
AU  - Golla Saidulu
PY  - 2025
DA  - 2025/05/23
TI  - Machine Learning Models for Predicting Hospital Readmission Rates
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 1299
EP  - 1308
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_108
DO  - 10.2991/978-94-6463-718-2_108
ID  - Sravanthi2025
ER  -