AI-Enhanced Tele-Rehabilitation: Predictive Modeling for Fall Risk and Treatment Efficacy in Balance Disorders
This study focuses on the development of artificial intelligence models to enhance telerehabilitation practices. We utilized diverse datasets to create clinically relevant models for predicting two critical outcomes: fall risk and treatment effectiveness. By applying various machine learning techniques, including K-Nearest Neighbors, Random Forest, Decision Tree, Support Vector Machine, and XGBoost, our models demonstrated high accuracy, sensitivity, and specificity. Notably, the Random Forest model achieved an accuracy of 0.97 in predicting fall risk and 0.96 in assessing treatment effectiveness. These models equip clinicians with powerful tools for data-driven decision-making, ultimately improving patient outcomes in rehabilitation settings.
Authors
- Dimitrios Kikidis (ORCID: https://orcid.org/0000-0002-1445-5474)
- Marousa Pavlou (ORCID: https://orcid.org/0000-0002-5861-2865)
- Vassilis Tsakanikas (ORCID: https://orcid.org/0000-0002-2868-1720)
- Christos Nikitas (ORCID: https://orcid.org/0000-0003-2235-7978)
- Themis P. Exarchos (ORCID: https://orcid.org/0000-0001-9420-9419)
- Dimitrios I. Fotiadis (ORCID: https://orcid.org/0000-0002-5987-9350)
- Doris‐Eva Bamiou (ORCID: https://orcid.org/0000-0001-7486-4264)
- Efterpi Karapintzou
- Brooke Nairn (ORCID: https://orcid.org/0009-0002-4731-3090)
Institutions
- University College Hospital (GB)
- Ionian University (GR)
- University of Ioannina (GR)
- National and Kapodistrian University of Athens (GR)
- Hippocration General Hospital (GR)
- University College London (GB)
Publication Details
- Published
- 2024-11-27
- DOI
- https://doi.org/10.1109/bibe63649.2024.10820488
- Citations
- 2
- Primary Topic
- Stroke Rehabilitation and Recovery
- Type
- article
- Field-Weighted Citation Impact
- 0.50