Version 1
: Received: 16 September 2024 / Approved: 17 September 2024 / Online: 17 September 2024 (08:37:50 CEST)
Version 2
: Received: 16 October 2024 / Approved: 17 October 2024 / Online: 17 October 2024 (08:33:36 CEST)
How to cite:
Joseph, P.; Ali, H.; Matthew, D.; Thomas, A.; Jose, R.; Mayer, J.; Bekbolatova, M.; Devine, T.; Toma, M. Regressive Machine Learning for Real-Time Monitoring of Bed-Based Patients. Preprints2024, 2024091279. https://doi.org/10.20944/preprints202409.1279.v2
Joseph, P.; Ali, H.; Matthew, D.; Thomas, A.; Jose, R.; Mayer, J.; Bekbolatova, M.; Devine, T.; Toma, M. Regressive Machine Learning for Real-Time Monitoring of Bed-Based Patients. Preprints 2024, 2024091279. https://doi.org/10.20944/preprints202409.1279.v2
Joseph, P.; Ali, H.; Matthew, D.; Thomas, A.; Jose, R.; Mayer, J.; Bekbolatova, M.; Devine, T.; Toma, M. Regressive Machine Learning for Real-Time Monitoring of Bed-Based Patients. Preprints2024, 2024091279. https://doi.org/10.20944/preprints202409.1279.v2
APA Style
Joseph, P., Ali, H., Matthew, D., Thomas, A., Jose, R., Mayer, J., Bekbolatova, M., Devine, T., & Toma, M. (2024). Regressive Machine Learning for Real-Time Monitoring of Bed-Based Patients. Preprints. https://doi.org/10.20944/preprints202409.1279.v2
Chicago/Turabian Style
Joseph, P., Timothy Devine and Milan Toma. 2024 "Regressive Machine Learning for Real-Time Monitoring of Bed-Based Patients" Preprints. https://doi.org/10.20944/preprints202409.1279.v2
Abstract
This study introduces an ensemble model designed for real-time monitoring of bedridden patients.The model was developed using a unique dataset, specifically acquired for this study, that captures six typical movements. The dataset was balanced using the Synthetic Minority Over-sampling Technique, resulting in a diverse distribution of movement types. Three models were evaluated: a Decision Tree Regressor, a Gradient Boosting Regressor, and a Bagging Regressor. The Decision Tree Regressor achieved an accuracy of 0.892 and an R2 score of 1.0 on the training dataset, and 0.939 on the test dataset. The Boosting Regressor achieved an accuracy of 0.908 and an R2 score of 0.99 on the training dataset, and 0.943 on the test dataset. The Bagging Regressor was selected due to its superior performance and trade-offs such as computational cost and scalability. It achieved an accuracy of 0.950, an R2 score of 0.996 for the training data, and an R2 score of 0.959 for the test data. The study also employs K-Fold cross-validation and learning curves to validate the robustness of the Bagging Regressor model. The proposed system addresses practical implementation challenges in real-time monitoring, such as data latency and false positives/negatives, and is designed for seamless integration with hospital IT infrastructure. This research demonstrates the potential of machine learning to enhance patient safety in healthcare settings.
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.
