Enhancing Patient Recovery Outcomes: The Role of Random Forest Algorithms in Predictive Analytics

DOI

10.2991/978-94-6463-718-2_114How to use a DOI?

Keywords

Machine Learning; Random Forest classifier; Personalized healthcare; Predictive analytics

Abstract

Motil encourages on-time recovery of the patient during hospital stay because good clinical outcome and cost-effectiveness go hand in hand. With this approach, individual recovery strategies are generated using daily patient data. Using a Random Forest classifier, we leverage ML to investigate predictive potential for major dimensions of health; heart rate, blood pressure, pain, mobility, nutrition and mood. Historical patient data is leveraged to extract recovery patterns, allowing for the accurate prediction of when the patient will be able to be discharged from the hospital and how it compares to previous recovery trajectories. The model takes in daily preprocessed patient data and outputs a set of personalized recommendations for regimens of exercise, diet, pain management, and other techniques for improved recovery. This is embedded in clinical workflows so that the recommendations can be reviewed and acted upon by the healthcare professional. This approach, therefore, leads to faster recoveries, better alignment of treatment plans, and informs treatment length of stay decisions. Overall, this approach improves patient outcomes, reduces rehospitalization rates, leads to lower healthcare costs, all while encouraging continuous quality improvement in healthcare.

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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TY  - CONF
AU  - S. Venkatesan
AU  - T. Karthick
AU  - M. Fahad Khan
PY  - 2025
DA  - 2025/05/23
TI  - Enhancing Patient Recovery Outcomes: The Role of Random Forest Algorithms in Predictive Analytics
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 1368
EP  - 1377
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_114
DO  - 10.2991/978-94-6463-718-2_114
ID  - Venkatesan2025
ER  -