Use of machine learning models to predict older adult ground-level falls: uncovering factors and patterns

A Level I trauma center used machine learning algorithms to identify risk factors and patterns in falls among older adults, which constitute our greatest burden of traumatic admissions.

A retrospective analysis was conducted on 2,391 ground-level fall trauma admissions from 2017-2022 including variables related to demographics, and weather conditions at admission. Supervised learning models were developed to predict older adult vs younger counterpart falls. In this machine learning modality, we generated a Decision Tree, a Support Vector Machine Classifier Algorithm, and a Logistic Regression Model. Unsupervised learning methods uncover patterns or groupings in the dataset of older adult ground-level falls, which consists of 1,742 records from 2017-2022 trauma admissions including comorbidity variables. Unsupervised learning methods of Principal Components Analysis, Hierarchical Clustering, and Market Basket Analysis were employed.

All three supervised models found the female sex as an important variable in predicting older adult falls. Unsupervised learning identified discernible patterns and groupings, revealing that certain weather variables are associated with falls.

These machine learning modalities can shed light on what may be important risk factors for older adult falls and can help to target awareness and outreach.