Machine Learning Approach on Evaluating Predictive Factors of Fall-Related Injuries

Abstract

According to the Public Health Agency of Canada, falls account for 95% of all hip fractures in Canada; 20% of fall-related injury cases end in death. This thesis evaluates the predictive power of many variables to predict fall-related injuries. The dataset chosen was CCHS which is high dimensional and diverse. The use of Principal Component Analysis (PCA) and random forest was employed to determine the highest priority risk factors to include in the predictive model. The results show that it is possible to predict fall-related injuries with a sensitivity of 80% or higher using four predictors (frequency of consultations with medical doctor, food and vegetable consumption, height and monthly physical activity level of over 15 minutes). Alternatively, the same sensitivity can be reached using age, frequency of walking for exercise per 3 months, alcohol consumption and personal income. None of the predictive models reached an accuracy of 70% or higher.

Further work in studying nutritional diets that offer protection from incurring a fall related injury are also recommended. Since the predictors are behavioral determinants of health and have a high sensitivity but a low accuracy, population health interventions are recommended rather than individual-level interventions. Suggestions to improve accuracy of built models are also proposed.