Evaluation of Machine Learning Methods for Predicting Heart Failure Readmissions: A Comparative Analysis

Authors

  • Samit Shivadekar Department of CSEE, University of Maryland Baltimore County, USA
  • Ketan Shahapure Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, USA
  • Shivam Vibhute San Jose State University, San Jose, ‎California‎, United States
  • Ashley Dunn Manager, Religroup, University of Maryland, Baltimore County, USA

Keywords:

Regression, SVM, Heart Disease, Classifier, ML, algorithms

Abstract

A heart failure (HF) condition is a type of chronic cardiovascular disease that affects millions of people globally. It can lead to various symptoms and has a significant impact on the quality of life. Despite the advancements that have been made in treating this condition, it remains a major public health issue. One of the biggest challenges that HF management faces is the high number of readmissions. This issue contributes to the increasing of patients' outcomes and costs the healthcare system. Implementing effective interventions and identifying those at high risk of returning to the hospital can help lower the financial burden on the system. Through the use of machine learning techniques, researchers can now predict the likelihood of HF readmissions. These tools can analyze large datasets and provide a personalized diagnosis and treatment plan. There have been various studies that have examined the use of ML for predicting HF readmissions. The goal of this study is to analyze the various techniques used in predicting HF readmissions and provide a comprehensive analysis of their performance. Through a combination of data collected from various sources, including a diverse set of patients, we will be able to explore the performance of various ML algorithms. In addition to the algorithms' performance, we will also look into their impact on various parameters, such as model evaluation metrics, optimization techniques, and feature selection. The findings of this study will be used to inform policymakers and healthcare providers about the use of ML techniques to identify patients at high risk of HF readmissions. These insights can help them improve the quality of care for those with this condition and develop effective interventions. The objective of this study is to use the power of ML to improve the management of HF and reduce the burden of readmissions on both the patients and the healthcare systems.

Downloads

Download data is not yet available.

References

Micheline Kamber and Jian Pei Jiawei Han, Data Mining Concepts and Techniques, 2012.

Mohammed Abdul Khaled, Sateesh Kumar Pradhan and G. N. Dash, "A survey of data mining techniques on medical data for finding locally frequent diseases", International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, pp. 149-153, August 2013.

Christian Nordqvist. “Heart Disease: Definition, Causes, Research.” Medical News Today, April 7, 2016. http://www.medicalnewstoday.com/articles/237191.php.

Porimol Palma,"Heart diseases now a plague in Bangladesh",[online]Available: http://www.thedailystar.net/heart-diseases-now-a-plague-in-bangladesh-57098.December 2014

Abdur Razzak, "A questionnaire survey on infectious disease among hospital patients in Kushtia and Jhenaidah Bangladesh", International Journal of Genetics and Molecular Biology, vol. 3, no. 9, pp. 120-xxx, October 2011.

D. P. Shukla, Shamsher Bahadur Patel, and Ashish Kumar Sen, "A literature review in health informatics using data mining techniques", International Journal of Software & Hardware Research in Engineering, vol. 2, no. 2, February 2014.

V. Gayathri, M. Chanda Mona and S. Banu Chitra, "A survey of data mining techniques on medical diagnosis and research", International Journal of Data Engineering (IJDE) Singapore Journal of Scientific Research (SJSR), vol. 6, pp. 301-310, 2014.

M. Akhil Jabbar, Priti Chandra, and B. L Deekshatulu, "Heartdisease prediction system using associative classification and genetic algorithm", International Conference on Emerging Trends in Electrical Electronics and Communication Technologies-ICECIT, 2012.

R. Chitra and V. Seenivasagam, "Review of heart disease prediction system using data mining and hybrid intelligent", ICTACT Journal on Soft Computing, vol. 03, no. 04, July 2013.

Abhishek Taneja, "Heart disease prediction system using data mining techniques", Oriental Journal of Computer Science & Technology, vol. 6, pp. 457-466, December 2013.

Hlaudi Daniel Masethe and Mosima Anna Masethe, “Prediction of heart disease using classification algorithms", Proceeding of the World Congress on Engineering and Computer Science, vol. II, 2014.

H.D.UCI,” kral machine/analyzing-the-heart-disease /notebook, Heart Disease UCI, 2019.”[Online].Available: https://www.kaggle.com/kralmachine/analyzing-the-heart-disease/notebook. [Accessed 2020].

Ahmed Ismail, Samir Abdlerazek, I.M. El-Henawy, ”Big data analysis in heart disease prediction”,JATIT,15th June 2020. Vol.98. No 11

Adil Hussain Seh, Dr Pawan kumar Chaurasia,”A Review on Heart Disease Prediction Using Machine Learning Techniques “,IJMRA,Vol. 9 Issue 4, April 2019, ISSN: 2249-0558,

NHLBI,” Digital Investigation Group (DIG) data Documentation” NHLBI Study #995 Revised Feb 1992 https://biolincc.nhlbi.nih.gov/media/teachingstudies/digdoc.pdf?link_time=2020-08-21_12:31:27.479822

Dhabliya, D., Sharma, R. Cloud computing based mobile devices for distributed computing (2019) International Journal of Control and Automation, 12 (6 Special Issue), pp. 1-4.

Dhabliya, D., Parvez, A. Protocol and its benefits for secure shell (2019) International Journal of Control and Automation, 12 (6 Special Issue), pp. 19-23.

Downloads

Published

30.11.2023

How to Cite

Shivadekar, S. ., Shahapure, K. ., Vibhute, S. ., & Dunn, A. . (2023). Evaluation of Machine Learning Methods for Predicting Heart Failure Readmissions: A Comparative Analysis. International Journal of Intelligent Systems and Applications in Engineering, 12(6s), 694–699. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4008

Issue

Vol. 12 No. 6s (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.