Association of Fall-Related Injuries and Different Diagnoses in Older Adults of Ontario: A Machine Learning Approach

Falls are the leading cause of injury-related hospitalizations among older adults in Canada. This study aimed to identify the most informative diagnostic categories associated with fall-related injuries (FRIs) using three machine learning algorithms: decision tree, random forest, and extreme gradient boosting tree (XGBoost). Secondary data from two Ontario health administrative databases (NACRS, DAD) covering the period 2006-2015 were analyzed. Older adults (aged ≥ 65 years) who sought treatment for FRIs in emergency departments (ED) or hospitals, as indicated by Canadian version of the 10th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10-CA) codes for falls and injuries, were included in the study. Accuracy, sensitivity, specificity, precision, and F1 score measures were calculated for each model. A total of 631,339 ED admissions and 304,495 hospitalizations were recorded due to FRIs. The random forest model demonstrated the highest sensitivity and accuracy in both datasets. Dyspnea and secondary malignant neoplasm of liver and intrahepatic bile duct were the most informative ICD-10-CA code and disease for FRIs among older adults admitted to ED and hospitals. These findings indicate that machine learning models can also be used to study FRIs as they are capable of handling large datasets and providing a better than 60% accuracy. Also, diagnostic categories linked to FRIs have a potential to enhance healthcare providers ‘ability to prevent FRIs in the future.