abstract
- The increasing incidence of accidental falls in older adults, along with associated health impacts such as morbidity and mortality, has made this a public health concern worldwide. This article presents the preliminary results of a study aimed at contributing valuable information to ongoing efforts that use supervised machine learning to classify fall risk based on post-urography data. A public dataset containing posturography, sociodemographic, and clinical data from 147 adults (18-85 years) was used. Various machine learning models were used to classify the risk of falls. Random Forest and XGBoost models demonstrated good classification performance, with Random Forest consistently outperforming in terms of accuracy (0.84 ±0.04), F1 score (0.86 ± 0.03), and area under the ROC curve (0.93 ± 0.03). Our study contributes to the growing body of evidence supporting the adoption of machine learning methods for predictive modeling to assess the risk of falls, offering an effective tool for early detection and underscoring their potential to revolutionize fall risk assessment in public health.