@article{article_1446723, title={Artificial Intelligence in Healthcare: Fall Risk Assessment in Older Adults by Using Machine Learning Techniques}, journal={Politeknik Dergisi}, volume={28}, pages={1415–1423}, year={2025}, DOI={10.2339/politeknik.1446723}, author={Özden Gürcan, Gökçe and Gokdas, Hakan and Turan Kızıldoğan, Ebru}, keywords={Düşme Riski, Makine Öğrenimi, Lojistik Regresyon, Karar Ağacı, Rastgele Orman Algoritması}, abstract={There are many attempts to provide the elderly with a life more independently. One of the main problems facing people in this age group is fall events. Falls are one of the most common accidents among the elderly and may result in extended hospitalization and increased medical costs. The requirement for care services, such as fall detection, is increasing because of the growing population of elderly people. In this study, machine learning techniques- Logistic Regression, Random Forest, and Decision Tree are used to predict fall risk of elderly people. Fall risk assessment methods are used to obtain inputs and outputs in addition to the physical and clinical features of people in the dataset.
This study aimed to facilitate the fall risk assessment process of health professionals to determine the fall risk factors of elderly individuals and to make predictions. Based on the results of fall prediction, individualized fall prevention interventions can be developed to reduce the fall rates of elderly individuals.}, number={5}, publisher={Gazi Üniversitesi}