Objectives: Accurate prediction of fall risk in older adults is essential to prevent injuries and improve quality of life. This study evaluates the predictive performance of various machine learning models using accelerometric data, non-accelerometric data, aiming to improve predictive accuracy and identify key contributing variable. Methods: We applied random forest, XGBoost, AdaBoost, LightGBM, support vector regression (SVR), decision trees, and Bayesian ridge regression to a dataset of 146 older adults. Models were trained using accelerometric data (movement patterns) and non-accelerometric data (demographic and clinical variables). Results: Models trained on combined accelerometric and non-accelerometric data consistently outperformed those based on single data types. Bayesian ridge regression achieved the highest accuracy (MSE = 0.6746, R Non-accelerometric factors, including age and comorbidities, signi ficantly contributed to fall risk prediction. Conclusions: Integrating accelerometric and non-accelerometric data improves fall risk prediction accuracy in older adults. Bayesian ridge regression trained on combined datasets provides superior predictive power compared to traditional models. Future work should validate these models in larger, more diverse populations to enhance clinical applicability. HEALTH Volume 11: 1 - 16 DOI: 10.1177/20552076251331752 Introduction and related work Background on fall risk Falls among older adults are a major health concern, with one-third experiencing falls annually, and up to 20% resulting in serious injuries such as fractures or head trauma. This problem is compounded by an aging population and places a significant economic burden on healthcare systems, exceeding 2 billion dollars annually in countries like Canada. Beyond physical injuries, falls reduce functional independence and quality of life. They often lead to prolonged hospitalizations, institutionalization, and increased mortality. Additionally, the fear of falling can discourage physical activity, creating a cycle of physical decline that further elevates fall risk. The fi nancial burden of falls is expected to increase as populations age, reinforcing the urgent need for effective fall prevention and improved risk prediction methods to mitigate both health and economic consequences.

Artificial intelligence (AI) is a powerful tool for reshaping healthcare systems. In healthcare, AI is invaluable for its capacity to manage vast amounts of data, which can lead to more accurate and speedy diagnoses, ultimately easing the workload on healthcare professionals. As a result, AI has proven itself to be a power tool across various industries, simplifying complex tasks and pattern recognition that would otherwise be overwhelming for humans or traditional computer algorithms. In this paper, we review the strengths and weaknesses of Bayesian Ridge Regression, an AI model that can be used to bring cutting edge virus analysis to healthcare professionals around the world. The model's accuracy assessment revealed promising results, with room for improvement primarily related to data organization. In addition, the severity index serves as a valuable tool to gain a broad overview of patient care needs, aligning with healthcare professionals' preference for broader categorizations.