Version 1
: Received: 23 January 2024 / Approved: 23 January 2024 / Online: 24 January 2024 (02:47:15 CET)
How to cite:
Hidayaturrohman, Q.A.; Hanada, E. Predictive Analytics in Heart Failure Prediction: A Systematic Review. Preprints2024, 2024011671. https://doi.org/10.20944/preprints202401.1671.v1
Hidayaturrohman, Q.A.; Hanada, E. Predictive Analytics in Heart Failure Prediction: A Systematic Review. Preprints 2024, 2024011671. https://doi.org/10.20944/preprints202401.1671.v1
Hidayaturrohman, Q.A.; Hanada, E. Predictive Analytics in Heart Failure Prediction: A Systematic Review. Preprints2024, 2024011671. https://doi.org/10.20944/preprints202401.1671.v1
APA Style
Hidayaturrohman, Q.A., & Hanada, E. (2024). Predictive Analytics in Heart Failure Prediction: A Systematic Review. Preprints. https://doi.org/10.20944/preprints202401.1671.v1
Chicago/Turabian Style
Hidayaturrohman, Q.A. and Eisuke Hanada. 2024 "Predictive Analytics in Heart Failure Prediction: A Systematic Review" Preprints. https://doi.org/10.20944/preprints202401.1671.v1
Abstract
Heart failure is a leading cause of death among people worldwide. The cost of treatment can be prohibitive, and early prediction of heart failure would reduce treatment costs to patients and hospitals. Improved prediction of readmission would also be of great help hospital, allowing them to better manage their treatment programs and budgets. This systematic review aims to summarize recent studies of predictive analytics models that have been constructed to predict heart failure and readmission. Some of these models use a statistical approach and others a machine learning approach. Electronic patient health records, including demography, physical and clinical values, and laboratory findings are used to design and build predictive models. Predictive analytics with good performance have become essential for clinicians and specialists. Choosing a suitable machine learning algorithm and data preprocessing technique will improve predictive model performance, including data imputation and class imbalance.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
